Nome: João Gabriel Andrade de Araujo Josephik NUSP: 12542265
Link do colab:https://colab.research.google.com/drive/1bqgY4qfsSZ_MjZp6Kuyh2t3Xdhoj5qLj?usp=sharing
Resultados do treinamento: https://drive.google.com/drive/folders/1xAvFkAXS75v5IMXeBfEjeAGT3dT3Wpcq?usp=share_link
O notebook abaixo contém a resolução de um problema de segmentação de vasos em iamgens de retinas. Os resultados foram bastante satisfatórios: 96% de acurácia no conjunto de testes. As redes foram capazes de detectar vasos bastante difÃceis para o olho humano.
Para ajudar a rede a aprender, foram utilizadas máscaras com diferentes pesos para cada pixel. Como o problema é desbalanceado, os vasos receberam peso proporcional para compensar esse desbalanceamento (em torno de 11). Além disso os pixels em volta dos vasos também receberam peso aumentado (é mais importante para a rede que os vasos sejam nÃtidos do que os pixels longe dos vasos!).
As redes utilizadas utilizam arquiteturas do tipo U-NET (com encoder e decoder). Foram testadas redes de dois tamanhos: aproximadamente 400 mil parâmetros e aproximadamente 8 milhões de parâmetros. Ambos modelos alcançaram resultados similares: 0.68046 contra 0.67319 de perda, respectivamente. Entretanto, o modelo maior alcançou o resultado mais rapidamente.
Early stopping foi essencial em ambos modelos: os dois sofreram de gravÃssimo overfitting. Isso pode ser percebido nos gráficos de treinamento.
Além disso, foi percebido que a adição de Batch Normalizaion ajudou enormemente com o treinamento.
Várias fontes foram consultadas na confecção desse trabalho. Todos os links estão listados abaixo.
import os
import gc
import torch
from torch import nn, optim
import pandas as pd
from skimage import io, transform, morphology
from skimage.morphology import binary_dilation, star
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch
from torch.optim import lr_scheduler as schd
from tqdm import tqdm
!pip install imagecodecs
Requirement already satisfied: imagecodecs in /usr/local/lib/python3.10/dist-packages (2023.9.18) Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imagecodecs) (1.23.5)
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Exploração dos dados¶
class DRIVE_Dataset(Dataset):
def __init__(self, data_dir, train=True, input_transform=None, target_transform=None, weight=1):
self.training = 'training'if train else 'test'
self.data_dir = os.path.join(data_dir, self.training)
self.input_dir=os.path.join(self.data_dir, 'input')
self.input=sorted([os.path.join(self.input_dir, image) for image in os.listdir(self.input_dir)])
self.target_dir=os.path.join(self.data_dir, 'target')
self.target=sorted([os.path.join(self.target_dir, image) for image in os.listdir(self.target_dir)])
assert len(self.input) == len(self.target)
self.input_transform = input_transform
self.target_transform = target_transform
self.to_tensor = transforms.ToTensor()
self.conv_mask = torch.ones((1, 1, 11, 11)) * (3/(11*11))
self.blur = lambda x: torch.nn.functional.conv2d(input=x.unsqueeze(0), weight=self.conv_mask, stride=1, padding='same').squeeze(0)
self.weight=weight
def __len__(self):
return len(self.input)
def __getitem__(self, idx):
with torch.no_grad():
input_im = self.to_tensor(io.imread(self.input[idx]))
target_im = self.to_tensor((io.imread(self.target[idx]))).type(torch.LongTensor).squeeze(0)
weight_im = self.blur((target_im.clone().type(torch.FloatTensor).unsqueeze(0) * self.weight)).squeeze(0).clamp(max=self.weight, min=1)
weight_im[target_im == 1] = self.weight
if self.input_transform:
input_im = self.input_transform(input_im)
if self.target_transform:
target_im = self.target_transform(target_im)
return (input_im, target_im, weight_im)
train_data=DRIVE_Dataset('/content/drive/MyDrive/dataset/drive',weight=10.5853)
test_data=DRIVE_Dataset('/content/drive/MyDrive/dataset/drive',weight=10.5853,train=False)
if train_data.weight==1:
weight=0
total=0
positive=1
for (input, target, _) in tqdm(train_data):
total+=target.shape[-1]*target.shape[-2]
positive+=target.sum()
weight=((total-positive)/positive)
test_data.weight=train_data.weight=weight
target=train_data[1][1]
weight=train_data[1][2]
print(target.shape)
plt.subplot(1, 2, 1)
plt.imshow(target,cmap='gray')
plt.subplot(1, 2, 2)
plt.imshow(weight, cmap='gray', vmin=0)
weight.max()
torch.Size([584, 565])
tensor(10.5853)
import matplotlib.pyplot as plt
length=3
begin=3
visualization_range = np.random.choice(len(train_data), length)
example_data = torch.stack([train_data[i][0] for i in visualization_range])
example_targets = torch.stack([train_data[i][1] for i in visualization_range])
example_weights = torch.stack([train_data[i][2] for i in visualization_range])
fig = plt.figure()
print(example_data.shape)
for i in range(3):
print(example_data[i].shape)
data = example_data[i].movedim(0, 2)
print(data.shape)
plt.subplot(3,3,3*i+1)
plt.tight_layout()
plt.imshow(data)
plt.xticks([])
plt.yticks([])
print(example_targets[i].shape)
target = example_targets[i]
print (target.shape)
plt.subplot(3,3,3*i+2)
plt.tight_layout()
plt.imshow(target, cmap='gray')
plt.xticks([])
plt.yticks([])
print(torch.unique(target))
weight=example_weights[i]
print (weight.shape)
plt.subplot(3,3,3*i+2)
plt.tight_layout()
plt.imshow(weight, cmap='gray', )
plt.xticks([])
plt.yticks([])
torch.Size([3, 3, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([584, 565])
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def train(network, train_loader, epoch, optimizer, loss_fn, log_interval, batch_size_train, train_losses, train_counter):
network.train()
for batch_idx, (data, target, weight) in enumerate(train_loader):
gc.collect()
torch.cuda.empty_cache()
data = data.to(device)
target = target.to(device)
weight = weight.to(device)
optimizer.zero_grad()
output = network(data)
loss = loss_fn(output, target)
loss = loss * weight
loss = loss.mean()
loss.backward()
optimizer.step()
data = None
target = None
weight = None
gc.collect()
torch.cuda.empty_cache()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * batch_size_train, len(train_loader.dataset),
100 * batch_idx / len(train_loader), loss))
train_losses.append(loss)
train_counter.append((batch_idx*batch_size_train) + ((epoch-1)*len(train_loader.dataset)))
def test(network, test_loader, loss_fn, batch_size_test, test_losses, test_counter):
network.eval()
test_loss = 0
correct = 0
batches=0
total=0
with torch.no_grad():
for data, target, weight in test_loader:
gc.collect()
torch.cuda.empty_cache()
data = data.to(device)
target = target.to(device)
weight = weight.to(device)
output = network(data)
loss = loss_fn(output, target ) * weight
test_loss += loss.sum()
pred = (output.argmax(dim=-3))
correct += pred.eq(target).sum()
total += target.numel()
test_loss = test_loss / total
test_losses.append(test_loss)
test_counter.append((test_counter[-1] + len(test_loader.dataset))if len(test_counter) > 0 else 0)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, total,
100 * correct / total ) )
fig = plt.figure()
ind = np.random.randint(0, len(test_loader.dataset))
input=test_loader.dataset[ind][0]
target=test_loader.dataset[ind][1]
plt.subplot(1,3,1)
plt.imshow(input.movedim(0, 2))
plt.subplot(1,3,2)
plt.imshow(target, cmap='gray')
plt.subplot(1,3,3)
output=network(input.unsqueeze(0).to(device)).cpu().squeeze(0).argmax(-3)
plt.imshow(output, cmap='gray', vmin=0, vmax=1)
plt.show()
Rede menor¶
class UNetBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.seq=nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding='same'),
nn.ReLU(),
nn.BatchNorm2d(out_channels),
nn.Conv2d(out_channels, out_channels, 3, padding='same'),
nn.ReLU())
def forward(self, x):
return self.seq(x)
class UpConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.ct= nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2),
nn.ReLU())
def forward(self, x, i):
o = self.ct(x)
padding = [0, 0, 0, 0]
if o.shape[-1] == (i.shape[-1] - 1):
padding[0] = 1
if o.shape[-2] == (i.shape[-2] - 1):
padding[2] = 1
o = torch.nn.functional.pad(o, padding, mode='replicate')
return torch.cat([i, o], dim=1)
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.e1=UNetBlock(3, 64)
self.me1=nn.MaxPool2d(2)
self.bt=UNetBlock(64, 128)
self.ct1=UpConvBlock(128, 64)
self.d1=UNetBlock(128, 64)
self.cls = nn.Sequential(
nn.Conv2d(64, 2, kernel_size=1),
nn.ReLU()
)
def forward(self, x):
i1 = self.e1(x)
b = self.bt(self.me1(i1))
o1 = self.d1( self.ct1(b, i1) )
cls = self.cls(o1)
return cls
random_seed = 42
torch.manual_seed(random_seed)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
parameters_file='/content/drive/MyDrive/modelos/segmentacao_vasos/parameters.pth'
saved_parameters_file='/content/drive/MyDrive/modelos/segmentacao_vasos/best_parameters.pth'
training_data_file='/content/drive/MyDrive/modelos/segmentacao_vasos/treinamento.pickle'
batch_size_train = 8
batch_size_test = 8
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=batch_size_test, shuffle=True)
device
'cuda'
gc.collect()
torch.cuda.empty_cache()
n_epochs = 150
learning_rate = 1e-3
log_interval=1
network = UNet().to(device)
optimizer = optim.Adam(network.parameters(), lr=learning_rate)
loss_fn=nn.CrossEntropyLoss(reduction='none', )
scheduler = schd.StepLR(optimizer, 100, 0.5)
train_losses = []
train_counter = []
test_losses = []
test_counter=[]
count_parameters(network)
404354
test(network, test_loader, loss_fn, batch_size_test, test_losses, test_counter)
best=test_losses[-1]
for epoch in range(1, n_epochs + 1):
train(network, train_loader, epoch, optimizer, loss_fn, log_interval, batch_size_train, train_losses, train_counter)
test(network, test_loader, loss_fn, batch_size_test, test_losses, test_counter)
if test_losses[-1] < best:
best=test_losses[-1]
print("New best! Saving...")
torch.save(network.state_dict(), parameters_file)
scheduler.step()
torch.save((train_counter, train_losses, test_losses), training_data_file)
Test set: Avg. loss: 2.0280, Accuracy: 6021255/6599200 (91%)
Train Epoch: 1 [0/20 (0%)] Loss: 2.071117 Train Epoch: 1 [8/20 (33%)] Loss: 1.858218 Train Epoch: 1 [16/20 (67%)] Loss: 1.663678 Test set: Avg. loss: 2.0216, Accuracy: 6021255/6599200 (91%)
New best! Saving... Train Epoch: 2 [0/20 (0%)] Loss: 1.641148 Train Epoch: 2 [8/20 (33%)] Loss: 1.369758 Train Epoch: 2 [16/20 (67%)] Loss: 1.307152 Test set: Avg. loss: 1.9619, Accuracy: 6021255/6599200 (91%)
New best! Saving... Train Epoch: 3 [0/20 (0%)] Loss: 1.272263 Train Epoch: 3 [8/20 (33%)] Loss: 1.194274 Train Epoch: 3 [16/20 (67%)] Loss: 0.920055 Test set: Avg. loss: 1.9092, Accuracy: 6021255/6599200 (91%)
New best! Saving... Train Epoch: 4 [0/20 (0%)] Loss: 1.018035 Train Epoch: 4 [8/20 (33%)] Loss: 0.940074 Train Epoch: 4 [16/20 (67%)] Loss: 0.842104 Test set: Avg. loss: 1.9849, Accuracy: 6021255/6599200 (91%)
Train Epoch: 5 [0/20 (0%)] Loss: 0.837273 Train Epoch: 5 [8/20 (33%)] Loss: 0.992546 Train Epoch: 5 [16/20 (67%)] Loss: 0.789411 Test set: Avg. loss: 2.0387, Accuracy: 6021255/6599200 (91%)
Train Epoch: 6 [0/20 (0%)] Loss: 0.840231 Train Epoch: 6 [8/20 (33%)] Loss: 0.943322 Train Epoch: 6 [16/20 (67%)] Loss: 0.873625 Test set: Avg. loss: 2.0668, Accuracy: 6021255/6599200 (91%)
Train Epoch: 7 [0/20 (0%)] Loss: 0.831783 Train Epoch: 7 [8/20 (33%)] Loss: 0.833295 Train Epoch: 7 [16/20 (67%)] Loss: 0.954288 Test set: Avg. loss: 1.9886, Accuracy: 6021255/6599200 (91%)
Train Epoch: 8 [0/20 (0%)] Loss: 0.816984 Train Epoch: 8 [8/20 (33%)] Loss: 0.853048 Train Epoch: 8 [16/20 (67%)] Loss: 0.911765 Test set: Avg. loss: 2.0919, Accuracy: 6021255/6599200 (91%)
Train Epoch: 9 [0/20 (0%)] Loss: 0.805674 Train Epoch: 9 [8/20 (33%)] Loss: 0.846167 Train Epoch: 9 [16/20 (67%)] Loss: 0.855514 Test set: Avg. loss: 2.0332, Accuracy: 6021259/6599200 (91%)
Train Epoch: 10 [0/20 (0%)] Loss: 0.825857 Train Epoch: 10 [8/20 (33%)] Loss: 0.846431 Train Epoch: 10 [16/20 (67%)] Loss: 0.751011 Test set: Avg. loss: 2.2646, Accuracy: 6021256/6599200 (91%)
Train Epoch: 11 [0/20 (0%)] Loss: 0.781174 Train Epoch: 11 [8/20 (33%)] Loss: 0.856951 Train Epoch: 11 [16/20 (67%)] Loss: 0.856091 Test set: Avg. loss: 2.1725, Accuracy: 6021282/6599200 (91%)
Train Epoch: 12 [0/20 (0%)] Loss: 0.841683 Train Epoch: 12 [8/20 (33%)] Loss: 0.799109 Train Epoch: 12 [16/20 (67%)] Loss: 0.787187 Test set: Avg. loss: 2.2554, Accuracy: 6021345/6599200 (91%)
Train Epoch: 13 [0/20 (0%)] Loss: 0.810499 Train Epoch: 13 [8/20 (33%)] Loss: 0.807519 Train Epoch: 13 [16/20 (67%)] Loss: 0.732757 Test set: Avg. loss: 2.6010, Accuracy: 6021345/6599200 (91%)
Train Epoch: 14 [0/20 (0%)] Loss: 0.823066 Train Epoch: 14 [8/20 (33%)] Loss: 0.733828 Train Epoch: 14 [16/20 (67%)] Loss: 0.857558 Test set: Avg. loss: 2.3658, Accuracy: 6024026/6599200 (91%)
Train Epoch: 15 [0/20 (0%)] Loss: 0.836446 Train Epoch: 15 [8/20 (33%)] Loss: 0.750929 Train Epoch: 15 [16/20 (67%)] Loss: 0.776266 Test set: Avg. loss: 2.2784, Accuracy: 6030393/6599200 (91%)
Train Epoch: 16 [0/20 (0%)] Loss: 0.768602 Train Epoch: 16 [8/20 (33%)] Loss: 0.791097 Train Epoch: 16 [16/20 (67%)] Loss: 0.783455 Test set: Avg. loss: 2.3150, Accuracy: 6039258/6599200 (92%)
Train Epoch: 17 [0/20 (0%)] Loss: 0.741621 Train Epoch: 17 [8/20 (33%)] Loss: 0.869780 Train Epoch: 17 [16/20 (67%)] Loss: 0.672418 Test set: Avg. loss: 2.2044, Accuracy: 6070544/6599200 (92%)
Train Epoch: 18 [0/20 (0%)] Loss: 0.714636 Train Epoch: 18 [8/20 (33%)] Loss: 0.742592 Train Epoch: 18 [16/20 (67%)] Loss: 0.977906 Test set: Avg. loss: 1.9994, Accuracy: 6108795/6599200 (93%)
Train Epoch: 19 [0/20 (0%)] Loss: 0.821859 Train Epoch: 19 [8/20 (33%)] Loss: 0.752438 Train Epoch: 19 [16/20 (67%)] Loss: 0.745204 Test set: Avg. loss: 1.4720, Accuracy: 6193946/6599200 (94%)
New best! Saving... Train Epoch: 20 [0/20 (0%)] Loss: 0.755941 Train Epoch: 20 [8/20 (33%)] Loss: 0.803072 Train Epoch: 20 [16/20 (67%)] Loss: 0.710709 Test set: Avg. loss: 1.6024, Accuracy: 6182753/6599200 (94%)
Train Epoch: 21 [0/20 (0%)] Loss: 0.737611 Train Epoch: 21 [8/20 (33%)] Loss: 0.744719 Train Epoch: 21 [16/20 (67%)] Loss: 0.802404 Test set: Avg. loss: 1.3484, Accuracy: 6263401/6599200 (95%)
New best! Saving... Train Epoch: 22 [0/20 (0%)] Loss: 0.732900 Train Epoch: 22 [8/20 (33%)] Loss: 0.768913 Train Epoch: 22 [16/20 (67%)] Loss: 0.761322 Test set: Avg. loss: 1.2555, Accuracy: 6275955/6599200 (95%)
New best! Saving... Train Epoch: 23 [0/20 (0%)] Loss: 0.784656 Train Epoch: 23 [8/20 (33%)] Loss: 0.717574 Train Epoch: 23 [16/20 (67%)] Loss: 0.714265 Test set: Avg. loss: 1.1308, Accuracy: 6309100/6599200 (96%)
New best! Saving... Train Epoch: 24 [0/20 (0%)] Loss: 0.806895 Train Epoch: 24 [8/20 (33%)] Loss: 0.715950 Train Epoch: 24 [16/20 (67%)] Loss: 0.662165 Test set: Avg. loss: 1.0078, Accuracy: 6327095/6599200 (96%)
New best! Saving... Train Epoch: 25 [0/20 (0%)] Loss: 0.752773 Train Epoch: 25 [8/20 (33%)] Loss: 0.692406 Train Epoch: 25 [16/20 (67%)] Loss: 0.781059 Test set: Avg. loss: 0.9516, Accuracy: 6337297/6599200 (96%)
New best! Saving... Train Epoch: 26 [0/20 (0%)] Loss: 0.747632 Train Epoch: 26 [8/20 (33%)] Loss: 0.698897 Train Epoch: 26 [16/20 (67%)] Loss: 0.762367 Test set: Avg. loss: 0.8773, Accuracy: 6349346/6599200 (96%)
New best! Saving... Train Epoch: 27 [0/20 (0%)] Loss: 0.711204 Train Epoch: 27 [8/20 (33%)] Loss: 0.756166 Train Epoch: 27 [16/20 (67%)] Loss: 0.686098 Test set: Avg. loss: 0.8359, Accuracy: 6354454/6599200 (96%)
New best! Saving... Train Epoch: 28 [0/20 (0%)] Loss: 0.708682 Train Epoch: 28 [8/20 (33%)] Loss: 0.773028 Train Epoch: 28 [16/20 (67%)] Loss: 0.659033 Test set: Avg. loss: 0.8374, Accuracy: 6357018/6599200 (96%)
Train Epoch: 29 [0/20 (0%)] Loss: 0.718479 Train Epoch: 29 [8/20 (33%)] Loss: 0.700951 Train Epoch: 29 [16/20 (67%)] Loss: 0.730472 Test set: Avg. loss: 0.7864, Accuracy: 6356132/6599200 (96%)
New best! Saving... Train Epoch: 30 [0/20 (0%)] Loss: 0.729378 Train Epoch: 30 [8/20 (33%)] Loss: 0.662059 Train Epoch: 30 [16/20 (67%)] Loss: 0.766087 Test set: Avg. loss: 0.7563, Accuracy: 6352646/6599200 (96%)
New best! Saving... Train Epoch: 31 [0/20 (0%)] Loss: 0.747145 Train Epoch: 31 [8/20 (33%)] Loss: 0.657076 Train Epoch: 31 [16/20 (67%)] Loss: 0.723471 Test set: Avg. loss: 0.7712, Accuracy: 6362152/6599200 (96%)
Train Epoch: 32 [0/20 (0%)] Loss: 0.718582 Train Epoch: 32 [8/20 (33%)] Loss: 0.735441 Train Epoch: 32 [16/20 (67%)] Loss: 0.623811 Test set: Avg. loss: 0.7424, Accuracy: 6353112/6599200 (96%)
New best! Saving... Train Epoch: 33 [0/20 (0%)] Loss: 0.724835 Train Epoch: 33 [8/20 (33%)] Loss: 0.668225 Train Epoch: 33 [16/20 (67%)] Loss: 0.699157 Test set: Avg. loss: 0.7633, Accuracy: 6362907/6599200 (96%)
Train Epoch: 34 [0/20 (0%)] Loss: 0.656853 Train Epoch: 34 [8/20 (33%)] Loss: 0.708918 Train Epoch: 34 [16/20 (67%)] Loss: 0.768950 Test set: Avg. loss: 0.7301, Accuracy: 6341913/6599200 (96%)
New best! Saving... Train Epoch: 35 [0/20 (0%)] Loss: 0.704153 Train Epoch: 35 [8/20 (33%)] Loss: 0.700676 Train Epoch: 35 [16/20 (67%)] Loss: 0.657799 Test set: Avg. loss: 0.7902, Accuracy: 6355887/6599200 (96%)
Train Epoch: 36 [0/20 (0%)] Loss: 0.708659 Train Epoch: 36 [8/20 (33%)] Loss: 0.680314 Train Epoch: 36 [16/20 (67%)] Loss: 0.736084 Test set: Avg. loss: 0.7263, Accuracy: 6348072/6599200 (96%)
New best! Saving... Train Epoch: 37 [0/20 (0%)] Loss: 0.733652 Train Epoch: 37 [8/20 (33%)] Loss: 0.696877 Train Epoch: 37 [16/20 (67%)] Loss: 0.602963 Test set: Avg. loss: 0.7579, Accuracy: 6359791/6599200 (96%)
Train Epoch: 38 [0/20 (0%)] Loss: 0.748806 Train Epoch: 38 [8/20 (33%)] Loss: 0.654537 Train Epoch: 38 [16/20 (67%)] Loss: 0.651977 Test set: Avg. loss: 0.7324, Accuracy: 6361784/6599200 (96%)
Train Epoch: 39 [0/20 (0%)] Loss: 0.625696 Train Epoch: 39 [8/20 (33%)] Loss: 0.674623 Train Epoch: 39 [16/20 (67%)] Loss: 0.848940 Test set: Avg. loss: 0.7219, Accuracy: 6330396/6599200 (96%)
New best! Saving... Train Epoch: 40 [0/20 (0%)] Loss: 0.662682 Train Epoch: 40 [8/20 (33%)] Loss: 0.655151 Train Epoch: 40 [16/20 (67%)] Loss: 0.798999 Test set: Avg. loss: 0.7482, Accuracy: 6359651/6599200 (96%)
Train Epoch: 41 [0/20 (0%)] Loss: 0.718678 Train Epoch: 41 [8/20 (33%)] Loss: 0.682373 Train Epoch: 41 [16/20 (67%)] Loss: 0.593573 Test set: Avg. loss: 0.8324, Accuracy: 6366961/6599200 (96%)
Train Epoch: 42 [0/20 (0%)] Loss: 0.722515 Train Epoch: 42 [8/20 (33%)] Loss: 0.679373 Train Epoch: 42 [16/20 (67%)] Loss: 0.614607 Test set: Avg. loss: 0.7472, Accuracy: 6363163/6599200 (96%)
Train Epoch: 43 [0/20 (0%)] Loss: 0.673375 Train Epoch: 43 [8/20 (33%)] Loss: 0.677057 Train Epoch: 43 [16/20 (67%)] Loss: 0.690496 Test set: Avg. loss: 0.7213, Accuracy: 6365382/6599200 (96%)
New best! Saving... Train Epoch: 44 [0/20 (0%)] Loss: 0.663642 Train Epoch: 44 [8/20 (33%)] Loss: 0.668783 Train Epoch: 44 [16/20 (67%)] Loss: 0.678133 Test set: Avg. loss: 0.7271, Accuracy: 6359118/6599200 (96%)
Train Epoch: 45 [0/20 (0%)] Loss: 0.668505 Train Epoch: 45 [8/20 (33%)] Loss: 0.697063 Train Epoch: 45 [16/20 (67%)] Loss: 0.607149 Test set: Avg. loss: 0.7122, Accuracy: 6342234/6599200 (96%)
New best! Saving... Train Epoch: 46 [0/20 (0%)] Loss: 0.707093 Train Epoch: 46 [8/20 (33%)] Loss: 0.637736 Train Epoch: 46 [16/20 (67%)] Loss: 0.664821 Test set: Avg. loss: 0.7184, Accuracy: 6363339/6599200 (96%)
Train Epoch: 47 [0/20 (0%)] Loss: 0.687591 Train Epoch: 47 [8/20 (33%)] Loss: 0.645101 Train Epoch: 47 [16/20 (67%)] Loss: 0.674514 Test set: Avg. loss: 0.7037, Accuracy: 6351291/6599200 (96%)
New best! Saving... Train Epoch: 48 [0/20 (0%)] Loss: 0.679688 Train Epoch: 48 [8/20 (33%)] Loss: 0.648932 Train Epoch: 48 [16/20 (67%)] Loss: 0.659832 Test set: Avg. loss: 0.7114, Accuracy: 6346137/6599200 (96%)
Train Epoch: 49 [0/20 (0%)] Loss: 0.696961 Train Epoch: 49 [8/20 (33%)] Loss: 0.624510 Train Epoch: 49 [16/20 (67%)] Loss: 0.670850 Test set: Avg. loss: 0.7191, Accuracy: 6360766/6599200 (96%)
Train Epoch: 50 [0/20 (0%)] Loss: 0.717582 Train Epoch: 50 [8/20 (33%)] Loss: 0.600410 Train Epoch: 50 [16/20 (67%)] Loss: 0.651628 Test set: Avg. loss: 0.7069, Accuracy: 6346735/6599200 (96%)
Train Epoch: 51 [0/20 (0%)] Loss: 0.679178 Train Epoch: 51 [8/20 (33%)] Loss: 0.618201 Train Epoch: 51 [16/20 (67%)] Loss: 0.693349 Test set: Avg. loss: 0.7025, Accuracy: 6359448/6599200 (96%)
New best! Saving... Train Epoch: 52 [0/20 (0%)] Loss: 0.651858 Train Epoch: 52 [8/20 (33%)] Loss: 0.676165 Train Epoch: 52 [16/20 (67%)] Loss: 0.629525 Test set: Avg. loss: 0.7261, Accuracy: 6367480/6599200 (96%)
Train Epoch: 53 [0/20 (0%)] Loss: 0.641691 Train Epoch: 53 [8/20 (33%)] Loss: 0.685293 Train Epoch: 53 [16/20 (67%)] Loss: 0.632587 Test set: Avg. loss: 0.7111, Accuracy: 6342916/6599200 (96%)
Train Epoch: 54 [0/20 (0%)] Loss: 0.643004 Train Epoch: 54 [8/20 (33%)] Loss: 0.644728 Train Epoch: 54 [16/20 (67%)] Loss: 0.711221 Test set: Avg. loss: 0.6958, Accuracy: 6327491/6599200 (96%)
New best! Saving... Train Epoch: 55 [0/20 (0%)] Loss: 0.642007 Train Epoch: 55 [8/20 (33%)] Loss: 0.664477 Train Epoch: 55 [16/20 (67%)] Loss: 0.637142 Test set: Avg. loss: 0.7042, Accuracy: 6353598/6599200 (96%)
Train Epoch: 56 [0/20 (0%)] Loss: 0.657095 Train Epoch: 56 [8/20 (33%)] Loss: 0.671359 Train Epoch: 56 [16/20 (67%)] Loss: 0.578576 Test set: Avg. loss: 0.7540, Accuracy: 6370212/6599200 (97%)
Train Epoch: 57 [0/20 (0%)] Loss: 0.644365 Train Epoch: 57 [8/20 (33%)] Loss: 0.614531 Train Epoch: 57 [16/20 (67%)] Loss: 0.748394 Test set: Avg. loss: 0.6952, Accuracy: 6309391/6599200 (96%)
New best! Saving... Train Epoch: 58 [0/20 (0%)] Loss: 0.598908 Train Epoch: 58 [8/20 (33%)] Loss: 0.637614 Train Epoch: 58 [16/20 (67%)] Loss: 0.787209 Test set: Avg. loss: 0.6908, Accuracy: 6348155/6599200 (96%)
New best! Saving... Train Epoch: 59 [0/20 (0%)] Loss: 0.654586 Train Epoch: 59 [8/20 (33%)] Loss: 0.628184 Train Epoch: 59 [16/20 (67%)] Loss: 0.716931 Test set: Avg. loss: 0.7163, Accuracy: 6359713/6599200 (96%)
Train Epoch: 60 [0/20 (0%)] Loss: 0.685893 Train Epoch: 60 [8/20 (33%)] Loss: 0.646334 Train Epoch: 60 [16/20 (67%)] Loss: 0.607339 Test set: Avg. loss: 0.7130, Accuracy: 6364962/6599200 (96%)
Train Epoch: 61 [0/20 (0%)] Loss: 0.597035 Train Epoch: 61 [8/20 (33%)] Loss: 0.680704 Train Epoch: 61 [16/20 (67%)] Loss: 0.712835 Test set: Avg. loss: 0.6979, Accuracy: 6311476/6599200 (96%)
Train Epoch: 62 [0/20 (0%)] Loss: 0.579366 Train Epoch: 62 [8/20 (33%)] Loss: 0.738559 Train Epoch: 62 [16/20 (67%)] Loss: 0.613638 Test set: Avg. loss: 0.7039, Accuracy: 6354287/6599200 (96%)
Train Epoch: 63 [0/20 (0%)] Loss: 0.616358 Train Epoch: 63 [8/20 (33%)] Loss: 0.665361 Train Epoch: 63 [16/20 (67%)] Loss: 0.702747 Test set: Avg. loss: 0.6979, Accuracy: 6327589/6599200 (96%)
Train Epoch: 64 [0/20 (0%)] Loss: 0.620237 Train Epoch: 64 [8/20 (33%)] Loss: 0.683927 Train Epoch: 64 [16/20 (67%)] Loss: 0.603430 Test set: Avg. loss: 0.7298, Accuracy: 6367386/6599200 (96%)
Train Epoch: 65 [0/20 (0%)] Loss: 0.701467 Train Epoch: 65 [8/20 (33%)] Loss: 0.606259 Train Epoch: 65 [16/20 (67%)] Loss: 0.587339 Test set: Avg. loss: 0.7233, Accuracy: 6364281/6599200 (96%)
Train Epoch: 66 [0/20 (0%)] Loss: 0.614493 Train Epoch: 66 [8/20 (33%)] Loss: 0.701907 Train Epoch: 66 [16/20 (67%)] Loss: 0.584282 Test set: Avg. loss: 0.7129, Accuracy: 6342029/6599200 (96%)
Train Epoch: 67 [0/20 (0%)] Loss: 0.686623 Train Epoch: 67 [8/20 (33%)] Loss: 0.590915 Train Epoch: 67 [16/20 (67%)] Loss: 0.620969 Test set: Avg. loss: 0.6982, Accuracy: 6350729/6599200 (96%)
Train Epoch: 68 [0/20 (0%)] Loss: 0.625656 Train Epoch: 68 [8/20 (33%)] Loss: 0.662126 Train Epoch: 68 [16/20 (67%)] Loss: 0.616748 Test set: Avg. loss: 0.7027, Accuracy: 6346363/6599200 (96%)
Train Epoch: 69 [0/20 (0%)] Loss: 0.610321 Train Epoch: 69 [8/20 (33%)] Loss: 0.717737 Train Epoch: 69 [16/20 (67%)] Loss: 0.527016 Test set: Avg. loss: 0.6997, Accuracy: 6351925/6599200 (96%)
Train Epoch: 70 [0/20 (0%)] Loss: 0.584549 Train Epoch: 70 [8/20 (33%)] Loss: 0.667512 Train Epoch: 70 [16/20 (67%)] Loss: 0.689768 Test set: Avg. loss: 0.7050, Accuracy: 6319177/6599200 (96%)
Train Epoch: 71 [0/20 (0%)] Loss: 0.665051 Train Epoch: 71 [8/20 (33%)] Loss: 0.617857 Train Epoch: 71 [16/20 (67%)] Loss: 0.626945 Test set: Avg. loss: 0.7354, Accuracy: 6360367/6599200 (96%)
Train Epoch: 72 [0/20 (0%)] Loss: 0.587587 Train Epoch: 72 [8/20 (33%)] Loss: 0.665811 Train Epoch: 72 [16/20 (67%)] Loss: 0.677830 Test set: Avg. loss: 0.7272, Accuracy: 6361713/6599200 (96%)
Train Epoch: 73 [0/20 (0%)] Loss: 0.606406 Train Epoch: 73 [8/20 (33%)] Loss: 0.647205 Train Epoch: 73 [16/20 (67%)] Loss: 0.641260 Test set: Avg. loss: 0.7219, Accuracy: 6366552/6599200 (96%)
Train Epoch: 74 [0/20 (0%)] Loss: 0.610574 Train Epoch: 74 [8/20 (33%)] Loss: 0.646625 Train Epoch: 74 [16/20 (67%)] Loss: 0.623011 Test set: Avg. loss: 0.7180, Accuracy: 6369997/6599200 (97%)
Train Epoch: 75 [0/20 (0%)] Loss: 0.613848 Train Epoch: 75 [8/20 (33%)] Loss: 0.607866 Train Epoch: 75 [16/20 (67%)] Loss: 0.694491 Test set: Avg. loss: 0.7020, Accuracy: 6309526/6599200 (96%)
Train Epoch: 76 [0/20 (0%)] Loss: 0.600956 Train Epoch: 76 [8/20 (33%)] Loss: 0.676771 Train Epoch: 76 [16/20 (67%)] Loss: 0.567848 Test set: Avg. loss: 0.7086, Accuracy: 6355387/6599200 (96%)
Train Epoch: 77 [0/20 (0%)] Loss: 0.625827 Train Epoch: 77 [8/20 (33%)] Loss: 0.606135 Train Epoch: 77 [16/20 (67%)] Loss: 0.646898 Test set: Avg. loss: 0.6904, Accuracy: 6356248/6599200 (96%)
New best! Saving... Train Epoch: 78 [0/20 (0%)] Loss: 0.639858 Train Epoch: 78 [8/20 (33%)] Loss: 0.592940 Train Epoch: 78 [16/20 (67%)] Loss: 0.628368 Test set: Avg. loss: 0.6805, Accuracy: 6340587/6599200 (96%)
New best! Saving... Train Epoch: 79 [0/20 (0%)] Loss: 0.575834 Train Epoch: 79 [8/20 (33%)] Loss: 0.674066 Train Epoch: 79 [16/20 (67%)] Loss: 0.586719 Test set: Avg. loss: 0.6924, Accuracy: 6353541/6599200 (96%)
Train Epoch: 80 [0/20 (0%)] Loss: 0.549406 Train Epoch: 80 [8/20 (33%)] Loss: 0.683880 Train Epoch: 80 [16/20 (67%)] Loss: 0.662099 Test set: Avg. loss: 0.7141, Accuracy: 6364163/6599200 (96%)
Train Epoch: 81 [0/20 (0%)] Loss: 0.585141 Train Epoch: 81 [8/20 (33%)] Loss: 0.699615 Train Epoch: 81 [16/20 (67%)] Loss: 0.530043 Test set: Avg. loss: 0.7361, Accuracy: 6360045/6599200 (96%)
Train Epoch: 82 [0/20 (0%)] Loss: 0.639862 Train Epoch: 82 [8/20 (33%)] Loss: 0.578288 Train Epoch: 82 [16/20 (67%)] Loss: 0.616918 Test set: Avg. loss: 0.7043, Accuracy: 6355741/6599200 (96%)
Train Epoch: 83 [0/20 (0%)] Loss: 0.625543 Train Epoch: 83 [8/20 (33%)] Loss: 0.571865 Train Epoch: 83 [16/20 (67%)] Loss: 0.666468 Test set: Avg. loss: 0.6909, Accuracy: 6329568/6599200 (96%)
Train Epoch: 84 [0/20 (0%)] Loss: 0.576331 Train Epoch: 84 [8/20 (33%)] Loss: 0.645479 Train Epoch: 84 [16/20 (67%)] Loss: 0.605664 Test set: Avg. loss: 0.6959, Accuracy: 6353529/6599200 (96%)
Train Epoch: 85 [0/20 (0%)] Loss: 0.644544 Train Epoch: 85 [8/20 (33%)] Loss: 0.539787 Train Epoch: 85 [16/20 (67%)] Loss: 0.674202 Test set: Avg. loss: 0.7206, Accuracy: 6361295/6599200 (96%)
Train Epoch: 86 [0/20 (0%)] Loss: 0.608993 Train Epoch: 86 [8/20 (33%)] Loss: 0.621211 Train Epoch: 86 [16/20 (67%)] Loss: 0.641770 Test set: Avg. loss: 0.7196, Accuracy: 6329149/6599200 (96%)
Train Epoch: 87 [0/20 (0%)] Loss: 0.569024 Train Epoch: 87 [8/20 (33%)] Loss: 0.708869 Train Epoch: 87 [16/20 (67%)] Loss: 0.599506 Test set: Avg. loss: 0.7119, Accuracy: 6327500/6599200 (96%)
Train Epoch: 88 [0/20 (0%)] Loss: 0.587372 Train Epoch: 88 [8/20 (33%)] Loss: 0.625988 Train Epoch: 88 [16/20 (67%)] Loss: 0.693711 Test set: Avg. loss: 0.7220, Accuracy: 6251654/6599200 (95%)
Train Epoch: 89 [0/20 (0%)] Loss: 0.618407 Train Epoch: 89 [8/20 (33%)] Loss: 0.634159 Train Epoch: 89 [16/20 (67%)] Loss: 0.621957 Test set: Avg. loss: 0.8327, Accuracy: 6357066/6599200 (96%)
Train Epoch: 90 [0/20 (0%)] Loss: 0.607474 Train Epoch: 90 [8/20 (33%)] Loss: 0.683178 Train Epoch: 90 [16/20 (67%)] Loss: 0.567343 Test set: Avg. loss: 0.7772, Accuracy: 6358504/6599200 (96%)
Train Epoch: 91 [0/20 (0%)] Loss: 0.592536 Train Epoch: 91 [8/20 (33%)] Loss: 0.655280 Train Epoch: 91 [16/20 (67%)] Loss: 0.638833 Test set: Avg. loss: 0.7144, Accuracy: 6330476/6599200 (96%)
Train Epoch: 92 [0/20 (0%)] Loss: 0.673882 Train Epoch: 92 [8/20 (33%)] Loss: 0.591387 Train Epoch: 92 [16/20 (67%)] Loss: 0.586819 Test set: Avg. loss: 0.7405, Accuracy: 6363008/6599200 (96%)
Train Epoch: 93 [0/20 (0%)] Loss: 0.590731 Train Epoch: 93 [8/20 (33%)] Loss: 0.654422 Train Epoch: 93 [16/20 (67%)] Loss: 0.614364 Test set: Avg. loss: 0.6998, Accuracy: 6326787/6599200 (96%)
Train Epoch: 94 [0/20 (0%)] Loss: 0.627733 Train Epoch: 94 [8/20 (33%)] Loss: 0.574706 Train Epoch: 94 [16/20 (67%)] Loss: 0.722444 Test set: Avg. loss: 0.7404, Accuracy: 6357044/6599200 (96%)
Train Epoch: 95 [0/20 (0%)] Loss: 0.633978 Train Epoch: 95 [8/20 (33%)] Loss: 0.592496 Train Epoch: 95 [16/20 (67%)] Loss: 0.654060 Test set: Avg. loss: 0.7276, Accuracy: 6337572/6599200 (96%)
Train Epoch: 96 [0/20 (0%)] Loss: 0.597606 Train Epoch: 96 [8/20 (33%)] Loss: 0.624677 Train Epoch: 96 [16/20 (67%)] Loss: 0.654407 Test set: Avg. loss: 0.6945, Accuracy: 6353149/6599200 (96%)
Train Epoch: 97 [0/20 (0%)] Loss: 0.606649 Train Epoch: 97 [8/20 (33%)] Loss: 0.586079 Train Epoch: 97 [16/20 (67%)] Loss: 0.729222 Test set: Avg. loss: 0.7256, Accuracy: 6362541/6599200 (96%)
Train Epoch: 98 [0/20 (0%)] Loss: 0.585859 Train Epoch: 98 [8/20 (33%)] Loss: 0.623011 Train Epoch: 98 [16/20 (67%)] Loss: 0.650296 Test set: Avg. loss: 0.7013, Accuracy: 6303491/6599200 (96%)
Train Epoch: 99 [0/20 (0%)] Loss: 0.634204 Train Epoch: 99 [8/20 (33%)] Loss: 0.602926 Train Epoch: 99 [16/20 (67%)] Loss: 0.575615 Test set: Avg. loss: 0.7395, Accuracy: 6365860/6599200 (96%)
Train Epoch: 100 [0/20 (0%)] Loss: 0.623139 Train Epoch: 100 [8/20 (33%)] Loss: 0.624446 Train Epoch: 100 [16/20 (67%)] Loss: 0.542280 Test set: Avg. loss: 0.7291, Accuracy: 6369779/6599200 (97%)
Train Epoch: 101 [0/20 (0%)] Loss: 0.622985 Train Epoch: 101 [8/20 (33%)] Loss: 0.593954 Train Epoch: 101 [16/20 (67%)] Loss: 0.576572 Test set: Avg. loss: 0.6922, Accuracy: 6363022/6599200 (96%)
Train Epoch: 102 [0/20 (0%)] Loss: 0.590734 Train Epoch: 102 [8/20 (33%)] Loss: 0.603721 Train Epoch: 102 [16/20 (67%)] Loss: 0.576474 Test set: Avg. loss: 0.6857, Accuracy: 6348716/6599200 (96%)
Train Epoch: 103 [0/20 (0%)] Loss: 0.555300 Train Epoch: 103 [8/20 (33%)] Loss: 0.605145 Train Epoch: 103 [16/20 (67%)] Loss: 0.646781 Test set: Avg. loss: 0.6904, Accuracy: 6353815/6599200 (96%)
Train Epoch: 104 [0/20 (0%)] Loss: 0.599496 Train Epoch: 104 [8/20 (33%)] Loss: 0.568369 Train Epoch: 104 [16/20 (67%)] Loss: 0.606925 Test set: Avg. loss: 0.6932, Accuracy: 6351330/6599200 (96%)
Train Epoch: 105 [0/20 (0%)] Loss: 0.550929 Train Epoch: 105 [8/20 (33%)] Loss: 0.599156 Train Epoch: 105 [16/20 (67%)] Loss: 0.640028 Test set: Avg. loss: 0.6960, Accuracy: 6352159/6599200 (96%)
Train Epoch: 106 [0/20 (0%)] Loss: 0.592961 Train Epoch: 106 [8/20 (33%)] Loss: 0.581586 Train Epoch: 106 [16/20 (67%)] Loss: 0.572037 Test set: Avg. loss: 0.7042, Accuracy: 6355886/6599200 (96%)
Train Epoch: 107 [0/20 (0%)] Loss: 0.596404 Train Epoch: 107 [8/20 (33%)] Loss: 0.573256 Train Epoch: 107 [16/20 (67%)] Loss: 0.562497 Test set: Avg. loss: 0.6943, Accuracy: 6343706/6599200 (96%)
Train Epoch: 108 [0/20 (0%)] Loss: 0.552983 Train Epoch: 108 [8/20 (33%)] Loss: 0.631850 Train Epoch: 108 [16/20 (67%)] Loss: 0.547059 Test set: Avg. loss: 0.6990, Accuracy: 6341876/6599200 (96%)
Train Epoch: 109 [0/20 (0%)] Loss: 0.545306 Train Epoch: 109 [8/20 (33%)] Loss: 0.615459 Train Epoch: 109 [16/20 (67%)] Loss: 0.564682 Test set: Avg. loss: 0.6955, Accuracy: 6341812/6599200 (96%)
Train Epoch: 110 [0/20 (0%)] Loss: 0.518866 Train Epoch: 110 [8/20 (33%)] Loss: 0.604134 Train Epoch: 110 [16/20 (67%)] Loss: 0.647792 Test set: Avg. loss: 0.6949, Accuracy: 6325535/6599200 (96%)
Train Epoch: 111 [0/20 (0%)] Loss: 0.563351 Train Epoch: 111 [8/20 (33%)] Loss: 0.575374 Train Epoch: 111 [16/20 (67%)] Loss: 0.596205 Test set: Avg. loss: 0.6982, Accuracy: 6328719/6599200 (96%)
Train Epoch: 112 [0/20 (0%)] Loss: 0.588307 Train Epoch: 112 [8/20 (33%)] Loss: 0.559848 Train Epoch: 112 [16/20 (67%)] Loss: 0.583602 Test set: Avg. loss: 0.7116, Accuracy: 6356168/6599200 (96%)
Train Epoch: 113 [0/20 (0%)] Loss: 0.618438 Train Epoch: 113 [8/20 (33%)] Loss: 0.519891 Train Epoch: 113 [16/20 (67%)] Loss: 0.582871 Test set: Avg. loss: 0.7236, Accuracy: 6355753/6599200 (96%)
Train Epoch: 114 [0/20 (0%)] Loss: 0.521516 Train Epoch: 114 [8/20 (33%)] Loss: 0.612236 Train Epoch: 114 [16/20 (67%)] Loss: 0.593923 Test set: Avg. loss: 0.7040, Accuracy: 6333043/6599200 (96%)
Train Epoch: 115 [0/20 (0%)] Loss: 0.554163 Train Epoch: 115 [8/20 (33%)] Loss: 0.579969 Train Epoch: 115 [16/20 (67%)] Loss: 0.598883 Test set: Avg. loss: 0.7025, Accuracy: 6326740/6599200 (96%)
Train Epoch: 116 [0/20 (0%)] Loss: 0.591368 Train Epoch: 116 [8/20 (33%)] Loss: 0.578810 Train Epoch: 116 [16/20 (67%)] Loss: 0.511954 Test set: Avg. loss: 0.7027, Accuracy: 6338062/6599200 (96%)
Train Epoch: 117 [0/20 (0%)] Loss: 0.566890 Train Epoch: 117 [8/20 (33%)] Loss: 0.589620 Train Epoch: 117 [16/20 (67%)] Loss: 0.518798 Test set: Avg. loss: 0.7155, Accuracy: 6360511/6599200 (96%)
Train Epoch: 118 [0/20 (0%)] Loss: 0.596485 Train Epoch: 118 [8/20 (33%)] Loss: 0.557034 Train Epoch: 118 [16/20 (67%)] Loss: 0.518419 Test set: Avg. loss: 0.7043, Accuracy: 6345574/6599200 (96%)
Train Epoch: 119 [0/20 (0%)] Loss: 0.524175 Train Epoch: 119 [8/20 (33%)] Loss: 0.601063 Train Epoch: 119 [16/20 (67%)] Loss: 0.573927 Test set: Avg. loss: 0.7009, Accuracy: 6327677/6599200 (96%)
Train Epoch: 120 [0/20 (0%)] Loss: 0.530362 Train Epoch: 120 [8/20 (33%)] Loss: 0.574558 Train Epoch: 120 [16/20 (67%)] Loss: 0.610005 Test set: Avg. loss: 0.7075, Accuracy: 6335262/6599200 (96%)
Train Epoch: 121 [0/20 (0%)] Loss: 0.606614 Train Epoch: 121 [8/20 (33%)] Loss: 0.571333 Train Epoch: 121 [16/20 (67%)] Loss: 0.481327 Test set: Avg. loss: 0.7164, Accuracy: 6344074/6599200 (96%)
Train Epoch: 122 [0/20 (0%)] Loss: 0.609663 Train Epoch: 122 [8/20 (33%)] Loss: 0.520191 Train Epoch: 122 [16/20 (67%)] Loss: 0.557410 Test set: Avg. loss: 0.7278, Accuracy: 6354068/6599200 (96%)
Train Epoch: 123 [0/20 (0%)] Loss: 0.523964 Train Epoch: 123 [8/20 (33%)] Loss: 0.579728 Train Epoch: 123 [16/20 (67%)] Loss: 0.584407 Test set: Avg. loss: 0.7085, Accuracy: 6330577/6599200 (96%)
Train Epoch: 124 [0/20 (0%)] Loss: 0.586841 Train Epoch: 124 [8/20 (33%)] Loss: 0.536515 Train Epoch: 124 [16/20 (67%)] Loss: 0.568403 Test set: Avg. loss: 0.7154, Accuracy: 6321539/6599200 (96%)
Train Epoch: 125 [0/20 (0%)] Loss: 0.579503 Train Epoch: 125 [8/20 (33%)] Loss: 0.563424 Train Epoch: 125 [16/20 (67%)] Loss: 0.580297 Test set: Avg. loss: 0.7234, Accuracy: 6346198/6599200 (96%)
Train Epoch: 126 [0/20 (0%)] Loss: 0.508271 Train Epoch: 126 [8/20 (33%)] Loss: 0.597698 Train Epoch: 126 [16/20 (67%)] Loss: 0.619862 Test set: Avg. loss: 0.7110, Accuracy: 6313600/6599200 (96%)
Train Epoch: 127 [0/20 (0%)] Loss: 0.581599 Train Epoch: 127 [8/20 (33%)] Loss: 0.553775 Train Epoch: 127 [16/20 (67%)] Loss: 0.559983 Test set: Avg. loss: 0.7052, Accuracy: 6320189/6599200 (96%)
Train Epoch: 128 [0/20 (0%)] Loss: 0.513882 Train Epoch: 128 [8/20 (33%)] Loss: 0.616198 Train Epoch: 128 [16/20 (67%)] Loss: 0.545204 Test set: Avg. loss: 0.7309, Accuracy: 6350066/6599200 (96%)
Train Epoch: 129 [0/20 (0%)] Loss: 0.560149 Train Epoch: 129 [8/20 (33%)] Loss: 0.523639 Train Epoch: 129 [16/20 (67%)] Loss: 0.643819 Test set: Avg. loss: 0.7153, Accuracy: 6335370/6599200 (96%)
Train Epoch: 130 [0/20 (0%)] Loss: 0.519638 Train Epoch: 130 [8/20 (33%)] Loss: 0.582814 Train Epoch: 130 [16/20 (67%)] Loss: 0.577603 Test set: Avg. loss: 0.7171, Accuracy: 6340603/6599200 (96%)
Train Epoch: 131 [0/20 (0%)] Loss: 0.520580 Train Epoch: 131 [8/20 (33%)] Loss: 0.559418 Train Epoch: 131 [16/20 (67%)] Loss: 0.630548 Test set: Avg. loss: 0.7270, Accuracy: 6338798/6599200 (96%)
Train Epoch: 132 [0/20 (0%)] Loss: 0.572061 Train Epoch: 132 [8/20 (33%)] Loss: 0.539691 Train Epoch: 132 [16/20 (67%)] Loss: 0.552215 Test set: Avg. loss: 0.7190, Accuracy: 6331266/6599200 (96%)
Train Epoch: 133 [0/20 (0%)] Loss: 0.540157 Train Epoch: 133 [8/20 (33%)] Loss: 0.562472 Train Epoch: 133 [16/20 (67%)] Loss: 0.562373 Test set: Avg. loss: 0.7267, Accuracy: 6352566/6599200 (96%)
Train Epoch: 134 [0/20 (0%)] Loss: 0.575377 Train Epoch: 134 [8/20 (33%)] Loss: 0.544436 Train Epoch: 134 [16/20 (67%)] Loss: 0.522691 Test set: Avg. loss: 0.7222, Accuracy: 6342667/6599200 (96%)
Train Epoch: 135 [0/20 (0%)] Loss: 0.549337 Train Epoch: 135 [8/20 (33%)] Loss: 0.561299 Train Epoch: 135 [16/20 (67%)] Loss: 0.527310 Test set: Avg. loss: 0.7331, Accuracy: 6341353/6599200 (96%)
Train Epoch: 136 [0/20 (0%)] Loss: 0.564622 Train Epoch: 136 [8/20 (33%)] Loss: 0.545674 Train Epoch: 136 [16/20 (67%)] Loss: 0.539318 Test set: Avg. loss: 0.7125, Accuracy: 6331127/6599200 (96%)
Train Epoch: 137 [0/20 (0%)] Loss: 0.532561 Train Epoch: 137 [8/20 (33%)] Loss: 0.564707 Train Epoch: 137 [16/20 (67%)] Loss: 0.528998 Test set: Avg. loss: 0.7103, Accuracy: 6332176/6599200 (96%)
Train Epoch: 138 [0/20 (0%)] Loss: 0.551501 Train Epoch: 138 [8/20 (33%)] Loss: 0.557656 Train Epoch: 138 [16/20 (67%)] Loss: 0.514159 Test set: Avg. loss: 0.7312, Accuracy: 6346037/6599200 (96%)
Train Epoch: 139 [0/20 (0%)] Loss: 0.579533 Train Epoch: 139 [8/20 (33%)] Loss: 0.506983 Train Epoch: 139 [16/20 (67%)] Loss: 0.554076 Test set: Avg. loss: 0.7498, Accuracy: 6338682/6599200 (96%)
Train Epoch: 140 [0/20 (0%)] Loss: 0.570441 Train Epoch: 140 [8/20 (33%)] Loss: 0.551852 Train Epoch: 140 [16/20 (67%)] Loss: 0.487429 Test set: Avg. loss: 0.7314, Accuracy: 6339063/6599200 (96%)
Train Epoch: 141 [0/20 (0%)] Loss: 0.555801 Train Epoch: 141 [8/20 (33%)] Loss: 0.549182 Train Epoch: 141 [16/20 (67%)] Loss: 0.513574 Test set: Avg. loss: 0.7338, Accuracy: 6345925/6599200 (96%)
Train Epoch: 142 [0/20 (0%)] Loss: 0.575291 Train Epoch: 142 [8/20 (33%)] Loss: 0.500881 Train Epoch: 142 [16/20 (67%)] Loss: 0.559738 Test set: Avg. loss: 0.7322, Accuracy: 6340747/6599200 (96%)
Train Epoch: 143 [0/20 (0%)] Loss: 0.563474 Train Epoch: 143 [8/20 (33%)] Loss: 0.518920 Train Epoch: 143 [16/20 (67%)] Loss: 0.538543 Test set: Avg. loss: 0.7440, Accuracy: 6335265/6599200 (96%)
Train Epoch: 144 [0/20 (0%)] Loss: 0.537539 Train Epoch: 144 [8/20 (33%)] Loss: 0.593093 Train Epoch: 144 [16/20 (67%)] Loss: 0.449065 Test set: Avg. loss: 0.7552, Accuracy: 6352635/6599200 (96%)
Train Epoch: 145 [0/20 (0%)] Loss: 0.563990 Train Epoch: 145 [8/20 (33%)] Loss: 0.493618 Train Epoch: 145 [16/20 (67%)] Loss: 0.592619 Test set: Avg. loss: 0.7305, Accuracy: 6339145/6599200 (96%)
Train Epoch: 146 [0/20 (0%)] Loss: 0.547299 Train Epoch: 146 [8/20 (33%)] Loss: 0.531166 Train Epoch: 146 [16/20 (67%)] Loss: 0.539570 Test set: Avg. loss: 0.7412, Accuracy: 6320419/6599200 (96%)
Train Epoch: 147 [0/20 (0%)] Loss: 0.508869 Train Epoch: 147 [8/20 (33%)] Loss: 0.535231 Train Epoch: 147 [16/20 (67%)] Loss: 0.613187 Test set: Avg. loss: 0.7450, Accuracy: 6339926/6599200 (96%)
Train Epoch: 148 [0/20 (0%)] Loss: 0.597988 Train Epoch: 148 [8/20 (33%)] Loss: 0.497355 Train Epoch: 148 [16/20 (67%)] Loss: 0.526544 Test set: Avg. loss: 0.7245, Accuracy: 6328605/6599200 (96%)
Train Epoch: 149 [0/20 (0%)] Loss: 0.553957 Train Epoch: 149 [8/20 (33%)] Loss: 0.493220 Train Epoch: 149 [16/20 (67%)] Loss: 0.601993 Test set: Avg. loss: 0.7375, Accuracy: 6334909/6599200 (96%)
Train Epoch: 150 [0/20 (0%)] Loss: 0.544388 Train Epoch: 150 [8/20 (33%)] Loss: 0.554787 Train Epoch: 150 [16/20 (67%)] Loss: 0.478928 Test set: Avg. loss: 0.7431, Accuracy: 6260855/6599200 (95%)
network.load_state_dict(torch.load(parameters_file))
fig = plt.figure()
length=5
begin=0
visualization_range = np.random.choice(len(test_data), length)
example_data = torch.stack([test_data[i][0] for i in visualization_range])
example_targets = torch.stack([test_data[i][1] for i in visualization_range])
network.eval()
plt.figure(figsize=(3*5, length*5))
for i in range(length):
print(example_data[i].shape)
data = example_data[i].movedim(0, 2)
print(data.shape)
plt.subplot(length,3,3*i+1)
plt.tight_layout()
plt.imshow(data)
plt.xticks([])
plt.yticks([])
target = example_targets[i]
print (target.shape)
plt.subplot(length,3,3*i+2)
plt.tight_layout()
plt.imshow(target, cmap='gray')
plt.xticks([])
plt.yticks([])
print(torch.unique(target))
with torch.no_grad():
output = network(example_data[i].to(device).unsqueeze(0)).squeeze(0)
print (output.shape)
plt.subplot(length,3,3*i+3)
plt.tight_layout()
plt.imshow(output.cpu().argmax(-3) , cmap='gray')
plt.xticks([])
plt.yticks([])
torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565])
<Figure size 640x480 with 0 Axes>
(train_counter, train_losses, test_losses) = torch.load( training_data_file)
fig = plt.figure()
test_counter = [i*len(train_loader.dataset) for i in range(len(test_losses))]
for i in range(len(train_losses)):
train_losses[i] = train_losses[i].cpu().detach()
for i in range(len(test_losses)):
test_losses[i] = test_losses[i].cpu().detach()
plt.plot(train_counter, train_losses, color='blue', label='Treinamento')
plt.plot(test_counter, test_losses, color='red', label='Teste')
plt.legend(['Train Loss', 'Test Loss'], loc='upper right')
plt.xlabel('number of training examples seen')
plt.ylabel('cross-entropy loss')
plt.legend()
plt.title("Rede convolucional")
print(f"Best: {min(test_losses)}")
Best: 0.6804683804512024
gc.collect()
torch.cuda.empty_cache()
Versão maior¶
class UNetBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.seq=nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding='same'),
nn.ReLU(),
nn.BatchNorm2d(out_channels),
nn.Conv2d(out_channels, out_channels, 3, padding='same'),
nn.ReLU())
def forward(self, x):
return self.seq(x)
class UpConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.ct= nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2),
nn.ReLU())
def forward(self, x, i):
o = self.ct(x)
padding = [0, 0, 0, 0]
if o.shape[-1] == (i.shape[-1] - 1):
padding[0] = 1
if o.shape[-2] == (i.shape[-2] - 1):
padding[2] = 1
o = torch.nn.functional.pad(o, padding, mode='replicate')
return torch.cat([i, o], dim=1)
class UNetGiga(nn.Module):
def __init__(self):
super().__init__()
self.e1=UNetBlock(3, 64)
self.me1=nn.MaxPool2d(2)
self.e2=UNetBlock(64, 128)
self.me2=nn.MaxPool2d(2)
self.e3=UNetBlock(128, 256)
self.me3=nn.MaxPool2d(2)
self.bt=UNetBlock(256, 512)
self.ct3=UpConvBlock(512, 256)
self.d3=UNetBlock(512, 256)
self.ct2=UpConvBlock(256, 128)
self.d2=UNetBlock(256, 128)
self.ct1=UpConvBlock(128, 64)
self.d1=UNetBlock(128, 64)
self.cls = nn.Sequential(
nn.Conv2d(64, 2, kernel_size=1),
nn.ReLU()
)
def forward(self, x):
i1 = self.e1(x)
i2 = self.e2(self.me1(i1))
i3 = self.e3(self.me2(i2))
b = self.bt(self.me3(i3))
o3 = self.d3( self.ct3(b, i3) )
o2 = self.d2( self.ct2(o3, i2) )
o1 = self.d1( self.ct1(o2, i1) )
cls = self.cls(o1)
return cls
random_seed = 42
torch.manual_seed(random_seed)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
parameters_file='/content/drive/MyDrive/modelos/segmentacao_vasos/parameters_giga.pth'
saved_parameters_file='/content/drive/MyDrive/modelos/segmentacao_vasos/best_parameters_giga.pth'
training_data_file='/content/drive/MyDrive/modelos/segmentacao_vasos/treinamento_giga.pickle'
batch_size_train = 2
batch_size_test = 2
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=batch_size_test, shuffle=True)
device
'cuda'
gc.collect()
torch.cuda.empty_cache()
n_epochs = 150
learning_rate = 1e-3
log_interval=1
network = UNetGiga().to(device)
optimizer = optim.Adam(network.parameters(), lr=learning_rate)
loss_fn=nn.CrossEntropyLoss(reduction='none', )
scheduler = schd.StepLR(optimizer, 100, 0.5)
train_losses = []
train_counter = []
test_losses = []
test_counter=[]
count_parameters(network)
7700226
test(network, test_loader, loss_fn, batch_size_test, test_losses, test_counter)
best=test_losses[-1]
for epoch in range(1, n_epochs + 1):
train(network, train_loader, epoch, optimizer, loss_fn, log_interval, batch_size_train, train_losses, train_counter)
test(network, test_loader, loss_fn, batch_size_test, test_losses, test_counter)
if test_losses[-1] < best:
best=test_losses[-1]
print("New best! Saving...")
torch.save(network.state_dict(), parameters_file)
scheduler.step()
torch.save((train_counter, train_losses, test_losses), training_data_file)
Test set: Avg. loss: 2.0398, Accuracy: 6021255/6599200 (91%)
Train Epoch: 1 [0/20 (0%)] Loss: 2.105670 Train Epoch: 1 [2/20 (10%)] Loss: 2.164086 Train Epoch: 1 [4/20 (20%)] Loss: 2.061682 Train Epoch: 1 [6/20 (30%)] Loss: 1.669649 Train Epoch: 1 [8/20 (40%)] Loss: 1.646256 Train Epoch: 1 [10/20 (50%)] Loss: 1.772807 Train Epoch: 1 [12/20 (60%)] Loss: 1.629969 Train Epoch: 1 [14/20 (70%)] Loss: 1.567791 Train Epoch: 1 [16/20 (80%)] Loss: 1.454288 Train Epoch: 1 [18/20 (90%)] Loss: 1.657349 Test set: Avg. loss: 1.9200, Accuracy: 6021255/6599200 (91%)
New best! Saving... Train Epoch: 2 [0/20 (0%)] Loss: 1.470815 Train Epoch: 2 [2/20 (10%)] Loss: 1.385474 Train Epoch: 2 [4/20 (20%)] Loss: 1.250381 Train Epoch: 2 [6/20 (30%)] Loss: 1.499206 Train Epoch: 2 [8/20 (40%)] Loss: 1.243076 Train Epoch: 2 [10/20 (50%)] Loss: 1.090109 Train Epoch: 2 [12/20 (60%)] Loss: 1.122425 Train Epoch: 2 [14/20 (70%)] Loss: 1.343699 Train Epoch: 2 [16/20 (80%)] Loss: 1.163055 Train Epoch: 2 [18/20 (90%)] Loss: 1.126568 Test set: Avg. loss: 2.3176, Accuracy: 6021255/6599200 (91%)
Train Epoch: 3 [0/20 (0%)] Loss: 1.083523 Train Epoch: 3 [2/20 (10%)] Loss: 1.038503 Train Epoch: 3 [4/20 (20%)] Loss: 1.163005 Train Epoch: 3 [6/20 (30%)] Loss: 1.296006 Train Epoch: 3 [8/20 (40%)] Loss: 1.140650 Train Epoch: 3 [10/20 (50%)] Loss: 0.961986 Train Epoch: 3 [12/20 (60%)] Loss: 1.146081 Train Epoch: 3 [14/20 (70%)] Loss: 1.275786 Train Epoch: 3 [16/20 (80%)] Loss: 1.015369 Train Epoch: 3 [18/20 (90%)] Loss: 1.069251 Test set: Avg. loss: 2.8096, Accuracy: 6021131/6599200 (91%)
Train Epoch: 4 [0/20 (0%)] Loss: 1.154545 Train Epoch: 4 [2/20 (10%)] Loss: 1.008595 Train Epoch: 4 [4/20 (20%)] Loss: 1.130684 Train Epoch: 4 [6/20 (30%)] Loss: 0.993621 Train Epoch: 4 [8/20 (40%)] Loss: 1.076076 Train Epoch: 4 [10/20 (50%)] Loss: 1.014167 Train Epoch: 4 [12/20 (60%)] Loss: 1.061702 Train Epoch: 4 [14/20 (70%)] Loss: 0.922724 Train Epoch: 4 [16/20 (80%)] Loss: 1.182678 Train Epoch: 4 [18/20 (90%)] Loss: 1.052276 Test set: Avg. loss: 2.9627, Accuracy: 6029738/6599200 (91%)
Train Epoch: 5 [0/20 (0%)] Loss: 0.917081 Train Epoch: 5 [2/20 (10%)] Loss: 1.058229 Train Epoch: 5 [4/20 (20%)] Loss: 1.103771 Train Epoch: 5 [6/20 (30%)] Loss: 1.074641 Train Epoch: 5 [8/20 (40%)] Loss: 1.037003 Train Epoch: 5 [10/20 (50%)] Loss: 1.050065 Train Epoch: 5 [12/20 (60%)] Loss: 1.004286 Train Epoch: 5 [14/20 (70%)] Loss: 1.013961 Train Epoch: 5 [16/20 (80%)] Loss: 0.976543 Train Epoch: 5 [18/20 (90%)] Loss: 1.237423 Test set: Avg. loss: 1.4697, Accuracy: 6225487/6599200 (94%)
New best! Saving... Train Epoch: 6 [0/20 (0%)] Loss: 0.984397 Train Epoch: 6 [2/20 (10%)] Loss: 0.929395 Train Epoch: 6 [4/20 (20%)] Loss: 0.984386 Train Epoch: 6 [6/20 (30%)] Loss: 1.128022 Train Epoch: 6 [8/20 (40%)] Loss: 0.991531 Train Epoch: 6 [10/20 (50%)] Loss: 1.018470 Train Epoch: 6 [12/20 (60%)] Loss: 0.947866 Train Epoch: 6 [14/20 (70%)] Loss: 1.225986 Train Epoch: 6 [16/20 (80%)] Loss: 0.930891 Train Epoch: 6 [18/20 (90%)] Loss: 1.020034 Test set: Avg. loss: 1.4405, Accuracy: 6256997/6599200 (95%)
New best! Saving... Train Epoch: 7 [0/20 (0%)] Loss: 0.922008 Train Epoch: 7 [2/20 (10%)] Loss: 1.002270 Train Epoch: 7 [4/20 (20%)] Loss: 1.171082 Train Epoch: 7 [6/20 (30%)] Loss: 0.858109 Train Epoch: 7 [8/20 (40%)] Loss: 1.063837 Train Epoch: 7 [10/20 (50%)] Loss: 0.985177 Train Epoch: 7 [12/20 (60%)] Loss: 1.017859 Train Epoch: 7 [14/20 (70%)] Loss: 0.908284 Train Epoch: 7 [16/20 (80%)] Loss: 1.073130 Train Epoch: 7 [18/20 (90%)] Loss: 0.987205 Test set: Avg. loss: 1.3621, Accuracy: 6266322/6599200 (95%)
New best! Saving... Train Epoch: 8 [0/20 (0%)] Loss: 0.890569 Train Epoch: 8 [2/20 (10%)] Loss: 1.145118 Train Epoch: 8 [4/20 (20%)] Loss: 1.108971 Train Epoch: 8 [6/20 (30%)] Loss: 0.932407 Train Epoch: 8 [8/20 (40%)] Loss: 1.011481 Train Epoch: 8 [10/20 (50%)] Loss: 0.880817 Train Epoch: 8 [12/20 (60%)] Loss: 0.957791 Train Epoch: 8 [14/20 (70%)] Loss: 0.881122 Train Epoch: 8 [16/20 (80%)] Loss: 0.903741 Train Epoch: 8 [18/20 (90%)] Loss: 1.105094 Test set: Avg. loss: 1.0789, Accuracy: 6348191/6599200 (96%)
New best! Saving... Train Epoch: 9 [0/20 (0%)] Loss: 0.812276 Train Epoch: 9 [2/20 (10%)] Loss: 0.949030 Train Epoch: 9 [4/20 (20%)] Loss: 0.847686 Train Epoch: 9 [6/20 (30%)] Loss: 0.904761 Train Epoch: 9 [8/20 (40%)] Loss: 0.922625 Train Epoch: 9 [10/20 (50%)] Loss: 1.010980 Train Epoch: 9 [12/20 (60%)] Loss: 1.055115 Train Epoch: 9 [14/20 (70%)] Loss: 1.191017 Train Epoch: 9 [16/20 (80%)] Loss: 0.917542 Train Epoch: 9 [18/20 (90%)] Loss: 0.994404 Test set: Avg. loss: 0.9846, Accuracy: 6339222/6599200 (96%)
New best! Saving... Train Epoch: 10 [0/20 (0%)] Loss: 0.878037 Train Epoch: 10 [2/20 (10%)] Loss: 0.952108 Train Epoch: 10 [4/20 (20%)] Loss: 0.816429 Train Epoch: 10 [6/20 (30%)] Loss: 0.950201 Train Epoch: 10 [8/20 (40%)] Loss: 1.106175 Train Epoch: 10 [10/20 (50%)] Loss: 0.887086 Train Epoch: 10 [12/20 (60%)] Loss: 1.027267 Train Epoch: 10 [14/20 (70%)] Loss: 0.850210 Train Epoch: 10 [16/20 (80%)] Loss: 0.876615 Train Epoch: 10 [18/20 (90%)] Loss: 0.760290 Test set: Avg. loss: 0.7889, Accuracy: 6320145/6599200 (96%)
New best! Saving... Train Epoch: 11 [0/20 (0%)] Loss: 0.781232 Train Epoch: 11 [2/20 (10%)] Loss: 0.936065 Train Epoch: 11 [4/20 (20%)] Loss: 0.876628 Train Epoch: 11 [6/20 (30%)] Loss: 0.722878 Train Epoch: 11 [8/20 (40%)] Loss: 0.773130 Train Epoch: 11 [10/20 (50%)] Loss: 0.793967 Train Epoch: 11 [12/20 (60%)] Loss: 0.654971 Train Epoch: 11 [14/20 (70%)] Loss: 0.763696 Train Epoch: 11 [16/20 (80%)] Loss: 0.676204 Train Epoch: 11 [18/20 (90%)] Loss: 0.625090 Test set: Avg. loss: 0.8101, Accuracy: 6358153/6599200 (96%)
Train Epoch: 12 [0/20 (0%)] Loss: 0.668097 Train Epoch: 12 [2/20 (10%)] Loss: 0.715646 Train Epoch: 12 [4/20 (20%)] Loss: 0.847722 Train Epoch: 12 [6/20 (30%)] Loss: 0.668351 Train Epoch: 12 [8/20 (40%)] Loss: 0.725165 Train Epoch: 12 [10/20 (50%)] Loss: 0.637833 Train Epoch: 12 [12/20 (60%)] Loss: 0.720396 Train Epoch: 12 [14/20 (70%)] Loss: 0.732089 Train Epoch: 12 [16/20 (80%)] Loss: 0.542707 Train Epoch: 12 [18/20 (90%)] Loss: 1.020309 Test set: Avg. loss: 0.7469, Accuracy: 6330758/6599200 (96%)
New best! Saving... Train Epoch: 13 [0/20 (0%)] Loss: 0.852706 Train Epoch: 13 [2/20 (10%)] Loss: 0.713532 Train Epoch: 13 [4/20 (20%)] Loss: 0.747767 Train Epoch: 13 [6/20 (30%)] Loss: 0.702934 Train Epoch: 13 [8/20 (40%)] Loss: 0.594547 Train Epoch: 13 [10/20 (50%)] Loss: 0.745275 Train Epoch: 13 [12/20 (60%)] Loss: 0.755904 Train Epoch: 13 [14/20 (70%)] Loss: 0.855514 Train Epoch: 13 [16/20 (80%)] Loss: 0.601410 Train Epoch: 13 [18/20 (90%)] Loss: 0.654908 Test set: Avg. loss: 0.8551, Accuracy: 6343739/6599200 (96%)
Train Epoch: 14 [0/20 (0%)] Loss: 0.726138 Train Epoch: 14 [2/20 (10%)] Loss: 0.567095 Train Epoch: 14 [4/20 (20%)] Loss: 0.548262 Train Epoch: 14 [6/20 (30%)] Loss: 0.937008 Train Epoch: 14 [8/20 (40%)] Loss: 0.674157 Train Epoch: 14 [10/20 (50%)] Loss: 0.797413 Train Epoch: 14 [12/20 (60%)] Loss: 0.706602 Train Epoch: 14 [14/20 (70%)] Loss: 0.827448 Train Epoch: 14 [16/20 (80%)] Loss: 0.698473 Train Epoch: 14 [18/20 (90%)] Loss: 0.661746 Test set: Avg. loss: 0.7502, Accuracy: 6353882/6599200 (96%)
Train Epoch: 15 [0/20 (0%)] Loss: 0.571726 Train Epoch: 15 [2/20 (10%)] Loss: 0.791332 Train Epoch: 15 [4/20 (20%)] Loss: 0.575318 Train Epoch: 15 [6/20 (30%)] Loss: 0.688489 Train Epoch: 15 [8/20 (40%)] Loss: 0.809585 Train Epoch: 15 [10/20 (50%)] Loss: 0.736191 Train Epoch: 15 [12/20 (60%)] Loss: 0.647049 Train Epoch: 15 [14/20 (70%)] Loss: 0.728662 Train Epoch: 15 [16/20 (80%)] Loss: 0.908928 Train Epoch: 15 [18/20 (90%)] Loss: 0.601269 Test set: Avg. loss: 1.0027, Accuracy: 5958536/6599200 (90%)
Train Epoch: 16 [0/20 (0%)] Loss: 0.695703 Train Epoch: 16 [2/20 (10%)] Loss: 0.871220 Train Epoch: 16 [4/20 (20%)] Loss: 0.702639 Train Epoch: 16 [6/20 (30%)] Loss: 0.701896 Train Epoch: 16 [8/20 (40%)] Loss: 0.638979 Train Epoch: 16 [10/20 (50%)] Loss: 0.704031 Train Epoch: 16 [12/20 (60%)] Loss: 0.561991 Train Epoch: 16 [14/20 (70%)] Loss: 0.562610 Train Epoch: 16 [16/20 (80%)] Loss: 0.810058 Train Epoch: 16 [18/20 (90%)] Loss: 0.659960 Test set: Avg. loss: 0.7429, Accuracy: 6354542/6599200 (96%)
New best! Saving... Train Epoch: 17 [0/20 (0%)] Loss: 0.678852 Train Epoch: 17 [2/20 (10%)] Loss: 0.703724 Train Epoch: 17 [4/20 (20%)] Loss: 0.730078 Train Epoch: 17 [6/20 (30%)] Loss: 0.617454 Train Epoch: 17 [8/20 (40%)] Loss: 0.647659 Train Epoch: 17 [10/20 (50%)] Loss: 0.697520 Train Epoch: 17 [12/20 (60%)] Loss: 0.596304 Train Epoch: 17 [14/20 (70%)] Loss: 0.852822 Train Epoch: 17 [16/20 (80%)] Loss: 0.598480 Train Epoch: 17 [18/20 (90%)] Loss: 0.703939 Test set: Avg. loss: 0.7334, Accuracy: 6350579/6599200 (96%)
New best! Saving... Train Epoch: 18 [0/20 (0%)] Loss: 0.636530 Train Epoch: 18 [2/20 (10%)] Loss: 0.594712 Train Epoch: 18 [4/20 (20%)] Loss: 0.549193 Train Epoch: 18 [6/20 (30%)] Loss: 0.825618 Train Epoch: 18 [8/20 (40%)] Loss: 0.677637 Train Epoch: 18 [10/20 (50%)] Loss: 0.885116 Train Epoch: 18 [12/20 (60%)] Loss: 0.643854 Train Epoch: 18 [14/20 (70%)] Loss: 0.692221 Train Epoch: 18 [16/20 (80%)] Loss: 0.741786 Train Epoch: 18 [18/20 (90%)] Loss: 0.675445 Test set: Avg. loss: 0.7523, Accuracy: 6358162/6599200 (96%)
Train Epoch: 19 [0/20 (0%)] Loss: 0.698953 Train Epoch: 19 [2/20 (10%)] Loss: 0.562919 Train Epoch: 19 [4/20 (20%)] Loss: 0.759970 Train Epoch: 19 [6/20 (30%)] Loss: 0.776136 Train Epoch: 19 [8/20 (40%)] Loss: 0.671269 Train Epoch: 19 [10/20 (50%)] Loss: 0.600075 Train Epoch: 19 [12/20 (60%)] Loss: 0.661862 Train Epoch: 19 [14/20 (70%)] Loss: 0.757075 Train Epoch: 19 [16/20 (80%)] Loss: 0.628448 Train Epoch: 19 [18/20 (90%)] Loss: 0.705342 Test set: Avg. loss: 0.7169, Accuracy: 6330636/6599200 (96%)
New best! Saving... Train Epoch: 20 [0/20 (0%)] Loss: 0.616377 Train Epoch: 20 [2/20 (10%)] Loss: 0.536991 Train Epoch: 20 [4/20 (20%)] Loss: 0.617309 Train Epoch: 20 [6/20 (30%)] Loss: 0.676868 Train Epoch: 20 [8/20 (40%)] Loss: 0.768271 Train Epoch: 20 [10/20 (50%)] Loss: 0.677520 Train Epoch: 20 [12/20 (60%)] Loss: 0.791770 Train Epoch: 20 [14/20 (70%)] Loss: 0.730436 Train Epoch: 20 [16/20 (80%)] Loss: 0.703602 Train Epoch: 20 [18/20 (90%)] Loss: 0.632822 Test set: Avg. loss: 0.7739, Accuracy: 6345202/6599200 (96%)
Train Epoch: 21 [0/20 (0%)] Loss: 0.559882 Train Epoch: 21 [2/20 (10%)] Loss: 0.615941 Train Epoch: 21 [4/20 (20%)] Loss: 0.736752 Train Epoch: 21 [6/20 (30%)] Loss: 0.652633 Train Epoch: 21 [8/20 (40%)] Loss: 0.622413 Train Epoch: 21 [10/20 (50%)] Loss: 0.687783 Train Epoch: 21 [12/20 (60%)] Loss: 0.575325 Train Epoch: 21 [14/20 (70%)] Loss: 0.814423 Train Epoch: 21 [16/20 (80%)] Loss: 0.775243 Train Epoch: 21 [18/20 (90%)] Loss: 0.749775 Test set: Avg. loss: 0.7418, Accuracy: 6321441/6599200 (96%)
Train Epoch: 22 [0/20 (0%)] Loss: 0.617726 Train Epoch: 22 [2/20 (10%)] Loss: 0.632367 Train Epoch: 22 [4/20 (20%)] Loss: 0.665147 Train Epoch: 22 [6/20 (30%)] Loss: 0.796845 Train Epoch: 22 [8/20 (40%)] Loss: 0.712521 Train Epoch: 22 [10/20 (50%)] Loss: 0.627745 Train Epoch: 22 [12/20 (60%)] Loss: 0.640818 Train Epoch: 22 [14/20 (70%)] Loss: 0.672726 Train Epoch: 22 [16/20 (80%)] Loss: 0.825129 Train Epoch: 22 [18/20 (90%)] Loss: 0.492900 Test set: Avg. loss: 0.6997, Accuracy: 6350931/6599200 (96%)
New best! Saving... Train Epoch: 23 [0/20 (0%)] Loss: 0.576227 Train Epoch: 23 [2/20 (10%)] Loss: 0.675530 Train Epoch: 23 [4/20 (20%)] Loss: 0.563903 Train Epoch: 23 [6/20 (30%)] Loss: 0.790953 Train Epoch: 23 [8/20 (40%)] Loss: 0.583363 Train Epoch: 23 [10/20 (50%)] Loss: 0.518545 Train Epoch: 23 [12/20 (60%)] Loss: 0.641941 Train Epoch: 23 [14/20 (70%)] Loss: 0.673156 Train Epoch: 23 [16/20 (80%)] Loss: 0.778241 Train Epoch: 23 [18/20 (90%)] Loss: 0.777882 Test set: Avg. loss: 0.6934, Accuracy: 6329484/6599200 (96%)
New best! Saving... Train Epoch: 24 [0/20 (0%)] Loss: 0.611133 Train Epoch: 24 [2/20 (10%)] Loss: 0.736893 Train Epoch: 24 [4/20 (20%)] Loss: 0.562401 Train Epoch: 24 [6/20 (30%)] Loss: 0.728837 Train Epoch: 24 [8/20 (40%)] Loss: 0.683733 Train Epoch: 24 [10/20 (50%)] Loss: 0.667450 Train Epoch: 24 [12/20 (60%)] Loss: 0.648190 Train Epoch: 24 [14/20 (70%)] Loss: 0.648288 Train Epoch: 24 [16/20 (80%)] Loss: 0.697141 Train Epoch: 24 [18/20 (90%)] Loss: 0.621366 Test set: Avg. loss: 0.6858, Accuracy: 6343405/6599200 (96%)
New best! Saving... Train Epoch: 25 [0/20 (0%)] Loss: 0.723391 Train Epoch: 25 [2/20 (10%)] Loss: 0.663200 Train Epoch: 25 [4/20 (20%)] Loss: 0.498854 Train Epoch: 25 [6/20 (30%)] Loss: 0.726870 Train Epoch: 25 [8/20 (40%)] Loss: 0.830787 Train Epoch: 25 [10/20 (50%)] Loss: 0.623004 Train Epoch: 25 [12/20 (60%)] Loss: 0.522999 Train Epoch: 25 [14/20 (70%)] Loss: 0.593595 Train Epoch: 25 [16/20 (80%)] Loss: 0.614227 Train Epoch: 25 [18/20 (90%)] Loss: 0.657833 Test set: Avg. loss: 0.7429, Accuracy: 6360684/6599200 (96%)
Train Epoch: 26 [0/20 (0%)] Loss: 0.737575 Train Epoch: 26 [2/20 (10%)] Loss: 0.673465 Train Epoch: 26 [4/20 (20%)] Loss: 0.663183 Train Epoch: 26 [6/20 (30%)] Loss: 0.772755 Train Epoch: 26 [8/20 (40%)] Loss: 0.618948 Train Epoch: 26 [10/20 (50%)] Loss: 0.615391 Train Epoch: 26 [12/20 (60%)] Loss: 0.809757 Train Epoch: 26 [14/20 (70%)] Loss: 0.556156 Train Epoch: 26 [16/20 (80%)] Loss: 0.530517 Train Epoch: 26 [18/20 (90%)] Loss: 0.616531 Test set: Avg. loss: 0.8191, Accuracy: 6356875/6599200 (96%)
Train Epoch: 27 [0/20 (0%)] Loss: 0.690262 Train Epoch: 27 [2/20 (10%)] Loss: 0.648077 Train Epoch: 27 [4/20 (20%)] Loss: 0.655596 Train Epoch: 27 [6/20 (30%)] Loss: 0.549793 Train Epoch: 27 [8/20 (40%)] Loss: 0.510494 Train Epoch: 27 [10/20 (50%)] Loss: 0.620349 Train Epoch: 27 [12/20 (60%)] Loss: 0.571916 Train Epoch: 27 [14/20 (70%)] Loss: 0.753619 Train Epoch: 27 [16/20 (80%)] Loss: 0.621126 Train Epoch: 27 [18/20 (90%)] Loss: 0.911958 Test set: Avg. loss: 0.6965, Accuracy: 6281706/6599200 (95%)
Train Epoch: 28 [0/20 (0%)] Loss: 0.565239 Train Epoch: 28 [2/20 (10%)] Loss: 0.573046 Train Epoch: 28 [4/20 (20%)] Loss: 0.643016 Train Epoch: 28 [6/20 (30%)] Loss: 0.633668 Train Epoch: 28 [8/20 (40%)] Loss: 0.736648 Train Epoch: 28 [10/20 (50%)] Loss: 0.560301 Train Epoch: 28 [12/20 (60%)] Loss: 0.645009 Train Epoch: 28 [14/20 (70%)] Loss: 0.768591 Train Epoch: 28 [16/20 (80%)] Loss: 0.620309 Train Epoch: 28 [18/20 (90%)] Loss: 0.627289 Test set: Avg. loss: 0.7246, Accuracy: 6361830/6599200 (96%)
Train Epoch: 29 [0/20 (0%)] Loss: 0.675237 Train Epoch: 29 [2/20 (10%)] Loss: 0.507669 Train Epoch: 29 [4/20 (20%)] Loss: 0.631135 Train Epoch: 29 [6/20 (30%)] Loss: 0.733098 Train Epoch: 29 [8/20 (40%)] Loss: 0.615009 Train Epoch: 29 [10/20 (50%)] Loss: 0.729404 Train Epoch: 29 [12/20 (60%)] Loss: 0.645356 Train Epoch: 29 [14/20 (70%)] Loss: 0.648683 Train Epoch: 29 [16/20 (80%)] Loss: 0.646414 Train Epoch: 29 [18/20 (90%)] Loss: 0.576197 Test set: Avg. loss: 0.9405, Accuracy: 6347675/6599200 (96%)
Train Epoch: 30 [0/20 (0%)] Loss: 0.573402 Train Epoch: 30 [2/20 (10%)] Loss: 0.638491 Train Epoch: 30 [4/20 (20%)] Loss: 0.534276 Train Epoch: 30 [6/20 (30%)] Loss: 0.771062 Train Epoch: 30 [8/20 (40%)] Loss: 0.912842 Train Epoch: 30 [10/20 (50%)] Loss: 0.617764 Train Epoch: 30 [12/20 (60%)] Loss: 0.672794 Train Epoch: 30 [14/20 (70%)] Loss: 0.565677 Train Epoch: 30 [16/20 (80%)] Loss: 0.605503 Train Epoch: 30 [18/20 (90%)] Loss: 0.661576 Test set: Avg. loss: 0.9141, Accuracy: 6341235/6599200 (96%)
Train Epoch: 31 [0/20 (0%)] Loss: 0.588730 Train Epoch: 31 [2/20 (10%)] Loss: 0.789165 Train Epoch: 31 [4/20 (20%)] Loss: 0.634984 Train Epoch: 31 [6/20 (30%)] Loss: 0.620180 Train Epoch: 31 [8/20 (40%)] Loss: 0.554321 Train Epoch: 31 [10/20 (50%)] Loss: 0.585654 Train Epoch: 31 [12/20 (60%)] Loss: 0.644523 Train Epoch: 31 [14/20 (70%)] Loss: 0.852553 Train Epoch: 31 [16/20 (80%)] Loss: 0.559279 Train Epoch: 31 [18/20 (90%)] Loss: 0.653717 Test set: Avg. loss: 0.8922, Accuracy: 6335348/6599200 (96%)
Train Epoch: 32 [0/20 (0%)] Loss: 0.611566 Train Epoch: 32 [2/20 (10%)] Loss: 0.532640 Train Epoch: 32 [4/20 (20%)] Loss: 0.638486 Train Epoch: 32 [6/20 (30%)] Loss: 0.542337 Train Epoch: 32 [8/20 (40%)] Loss: 0.643065 Train Epoch: 32 [10/20 (50%)] Loss: 0.567105 Train Epoch: 32 [12/20 (60%)] Loss: 0.667114 Train Epoch: 32 [14/20 (70%)] Loss: 0.733287 Train Epoch: 32 [16/20 (80%)] Loss: 0.798672 Train Epoch: 32 [18/20 (90%)] Loss: 0.627600 Test set: Avg. loss: 0.7089, Accuracy: 6303417/6599200 (96%)
Train Epoch: 33 [0/20 (0%)] Loss: 0.555539 Train Epoch: 33 [2/20 (10%)] Loss: 0.619189 Train Epoch: 33 [4/20 (20%)] Loss: 0.801939 Train Epoch: 33 [6/20 (30%)] Loss: 0.814699 Train Epoch: 33 [8/20 (40%)] Loss: 0.541990 Train Epoch: 33 [10/20 (50%)] Loss: 0.592173 Train Epoch: 33 [12/20 (60%)] Loss: 0.574697 Train Epoch: 33 [14/20 (70%)] Loss: 0.575335 Train Epoch: 33 [16/20 (80%)] Loss: 0.645288 Train Epoch: 33 [18/20 (90%)] Loss: 0.675509 Test set: Avg. loss: 0.7708, Accuracy: 6367630/6599200 (96%)
Train Epoch: 34 [0/20 (0%)] Loss: 0.469048 Train Epoch: 34 [2/20 (10%)] Loss: 0.586041 Train Epoch: 34 [4/20 (20%)] Loss: 0.723551 Train Epoch: 34 [6/20 (30%)] Loss: 0.608258 Train Epoch: 34 [8/20 (40%)] Loss: 0.582674 Train Epoch: 34 [10/20 (50%)] Loss: 0.645867 Train Epoch: 34 [12/20 (60%)] Loss: 0.505035 Train Epoch: 34 [14/20 (70%)] Loss: 0.806366 Train Epoch: 34 [16/20 (80%)] Loss: 0.608860 Train Epoch: 34 [18/20 (90%)] Loss: 0.697245 Test set: Avg. loss: 0.6826, Accuracy: 6361297/6599200 (96%)
New best! Saving... Train Epoch: 35 [0/20 (0%)] Loss: 0.574681 Train Epoch: 35 [2/20 (10%)] Loss: 0.621167 Train Epoch: 35 [4/20 (20%)] Loss: 0.582279 Train Epoch: 35 [6/20 (30%)] Loss: 0.704184 Train Epoch: 35 [8/20 (40%)] Loss: 0.710574 Train Epoch: 35 [10/20 (50%)] Loss: 0.567701 Train Epoch: 35 [12/20 (60%)] Loss: 0.745866 Train Epoch: 35 [14/20 (70%)] Loss: 0.574557 Train Epoch: 35 [16/20 (80%)] Loss: 0.506439 Train Epoch: 35 [18/20 (90%)] Loss: 0.559689 Test set: Avg. loss: 0.7765, Accuracy: 6365874/6599200 (96%)
Train Epoch: 36 [0/20 (0%)] Loss: 0.578967 Train Epoch: 36 [2/20 (10%)] Loss: 0.711453 Train Epoch: 36 [4/20 (20%)] Loss: 0.638442 Train Epoch: 36 [6/20 (30%)] Loss: 0.578443 Train Epoch: 36 [8/20 (40%)] Loss: 0.609891 Train Epoch: 36 [10/20 (50%)] Loss: 0.634616 Train Epoch: 36 [12/20 (60%)] Loss: 0.541595 Train Epoch: 36 [14/20 (70%)] Loss: 0.671892 Train Epoch: 36 [16/20 (80%)] Loss: 0.550130 Train Epoch: 36 [18/20 (90%)] Loss: 0.717529 Test set: Avg. loss: 0.6975, Accuracy: 6368086/6599200 (96%)
Train Epoch: 37 [0/20 (0%)] Loss: 0.491105 Train Epoch: 37 [2/20 (10%)] Loss: 0.596352 Train Epoch: 37 [4/20 (20%)] Loss: 0.773770 Train Epoch: 37 [6/20 (30%)] Loss: 0.645244 Train Epoch: 37 [8/20 (40%)] Loss: 0.723806 Train Epoch: 37 [10/20 (50%)] Loss: 0.576083 Train Epoch: 37 [12/20 (60%)] Loss: 0.722972 Train Epoch: 37 [14/20 (70%)] Loss: 0.587033 Train Epoch: 37 [16/20 (80%)] Loss: 0.557294 Train Epoch: 37 [18/20 (90%)] Loss: 0.580582 Test set: Avg. loss: 0.6906, Accuracy: 6367089/6599200 (96%)
Train Epoch: 38 [0/20 (0%)] Loss: 0.515208 Train Epoch: 38 [2/20 (10%)] Loss: 0.488327 Train Epoch: 38 [4/20 (20%)] Loss: 0.609587 Train Epoch: 38 [6/20 (30%)] Loss: 0.712473 Train Epoch: 38 [8/20 (40%)] Loss: 0.717610 Train Epoch: 38 [10/20 (50%)] Loss: 0.679291 Train Epoch: 38 [12/20 (60%)] Loss: 0.534442 Train Epoch: 38 [14/20 (70%)] Loss: 0.639153 Train Epoch: 38 [16/20 (80%)] Loss: 0.614304 Train Epoch: 38 [18/20 (90%)] Loss: 0.653875 Test set: Avg. loss: 0.8568, Accuracy: 6364398/6599200 (96%)
Train Epoch: 39 [0/20 (0%)] Loss: 0.627437 Train Epoch: 39 [2/20 (10%)] Loss: 0.536700 Train Epoch: 39 [4/20 (20%)] Loss: 0.596977 Train Epoch: 39 [6/20 (30%)] Loss: 0.582126 Train Epoch: 39 [8/20 (40%)] Loss: 0.491283 Train Epoch: 39 [10/20 (50%)] Loss: 0.681783 Train Epoch: 39 [12/20 (60%)] Loss: 0.635582 Train Epoch: 39 [14/20 (70%)] Loss: 0.701490 Train Epoch: 39 [16/20 (80%)] Loss: 0.486943 Train Epoch: 39 [18/20 (90%)] Loss: 0.707242 Test set: Avg. loss: 0.6929, Accuracy: 6374082/6599200 (97%)
Train Epoch: 40 [0/20 (0%)] Loss: 0.655734 Train Epoch: 40 [2/20 (10%)] Loss: 0.618674 Train Epoch: 40 [4/20 (20%)] Loss: 0.522207 Train Epoch: 40 [6/20 (30%)] Loss: 0.496951 Train Epoch: 40 [8/20 (40%)] Loss: 0.563476 Train Epoch: 40 [10/20 (50%)] Loss: 0.538300 Train Epoch: 40 [12/20 (60%)] Loss: 0.608936 Train Epoch: 40 [14/20 (70%)] Loss: 0.637689 Train Epoch: 40 [16/20 (80%)] Loss: 0.662078 Train Epoch: 40 [18/20 (90%)] Loss: 0.688938 Test set: Avg. loss: 0.6925, Accuracy: 6297465/6599200 (95%)
Train Epoch: 41 [0/20 (0%)] Loss: 0.607135 Train Epoch: 41 [2/20 (10%)] Loss: 0.565513 Train Epoch: 41 [4/20 (20%)] Loss: 0.640300 Train Epoch: 41 [6/20 (30%)] Loss: 0.629095 Train Epoch: 41 [8/20 (40%)] Loss: 0.571539 Train Epoch: 41 [10/20 (50%)] Loss: 0.531184 Train Epoch: 41 [12/20 (60%)] Loss: 0.759788 Train Epoch: 41 [14/20 (70%)] Loss: 0.725040 Train Epoch: 41 [16/20 (80%)] Loss: 0.509075 Train Epoch: 41 [18/20 (90%)] Loss: 0.502062 Test set: Avg. loss: 0.6901, Accuracy: 6377409/6599200 (97%)
Train Epoch: 42 [0/20 (0%)] Loss: 0.500021 Train Epoch: 42 [2/20 (10%)] Loss: 0.581652 Train Epoch: 42 [4/20 (20%)] Loss: 0.503729 Train Epoch: 42 [6/20 (30%)] Loss: 0.578263 Train Epoch: 42 [8/20 (40%)] Loss: 0.728907 Train Epoch: 42 [10/20 (50%)] Loss: 0.605899 Train Epoch: 42 [12/20 (60%)] Loss: 0.621944 Train Epoch: 42 [14/20 (70%)] Loss: 0.716379 Train Epoch: 42 [16/20 (80%)] Loss: 0.594709 Train Epoch: 42 [18/20 (90%)] Loss: 0.511211 Test set: Avg. loss: 0.7261, Accuracy: 6346050/6599200 (96%)
Train Epoch: 43 [0/20 (0%)] Loss: 0.600026 Train Epoch: 43 [2/20 (10%)] Loss: 0.582726 Train Epoch: 43 [4/20 (20%)] Loss: 0.681828 Train Epoch: 43 [6/20 (30%)] Loss: 0.552189 Train Epoch: 43 [8/20 (40%)] Loss: 0.555534 Train Epoch: 43 [10/20 (50%)] Loss: 0.581649 Train Epoch: 43 [12/20 (60%)] Loss: 0.702434 Train Epoch: 43 [14/20 (70%)] Loss: 0.736977 Train Epoch: 43 [16/20 (80%)] Loss: 0.567970 Train Epoch: 43 [18/20 (90%)] Loss: 0.444265 Test set: Avg. loss: 0.7686, Accuracy: 6385941/6599200 (97%)
Train Epoch: 44 [0/20 (0%)] Loss: 0.599822 Train Epoch: 44 [2/20 (10%)] Loss: 0.583611 Train Epoch: 44 [4/20 (20%)] Loss: 0.694799 Train Epoch: 44 [6/20 (30%)] Loss: 0.554990 Train Epoch: 44 [8/20 (40%)] Loss: 0.529561 Train Epoch: 44 [10/20 (50%)] Loss: 0.578887 Train Epoch: 44 [12/20 (60%)] Loss: 0.584580 Train Epoch: 44 [14/20 (70%)] Loss: 0.598975 Train Epoch: 44 [16/20 (80%)] Loss: 0.625494 Train Epoch: 44 [18/20 (90%)] Loss: 0.480313 Test set: Avg. loss: 0.6977, Accuracy: 6381967/6599200 (97%)
Train Epoch: 45 [0/20 (0%)] Loss: 0.623190 Train Epoch: 45 [2/20 (10%)] Loss: 0.569622 Train Epoch: 45 [4/20 (20%)] Loss: 0.695819 Train Epoch: 45 [6/20 (30%)] Loss: 0.501963 Train Epoch: 45 [8/20 (40%)] Loss: 0.574482 Train Epoch: 45 [10/20 (50%)] Loss: 0.503791 Train Epoch: 45 [12/20 (60%)] Loss: 0.578807 Train Epoch: 45 [14/20 (70%)] Loss: 0.527350 Train Epoch: 45 [16/20 (80%)] Loss: 0.685361 Train Epoch: 45 [18/20 (90%)] Loss: 0.526966 Test set: Avg. loss: 0.6954, Accuracy: 6362529/6599200 (96%)
Train Epoch: 46 [0/20 (0%)] Loss: 0.531112 Train Epoch: 46 [2/20 (10%)] Loss: 0.583269 Train Epoch: 46 [4/20 (20%)] Loss: 0.504495 Train Epoch: 46 [6/20 (30%)] Loss: 0.622752 Train Epoch: 46 [8/20 (40%)] Loss: 0.529666 Train Epoch: 46 [10/20 (50%)] Loss: 0.690172 Train Epoch: 46 [12/20 (60%)] Loss: 0.520774 Train Epoch: 46 [14/20 (70%)] Loss: 0.531847 Train Epoch: 46 [16/20 (80%)] Loss: 0.675119 Train Epoch: 46 [18/20 (90%)] Loss: 0.640681 Test set: Avg. loss: 0.6972, Accuracy: 6300382/6599200 (95%)
Train Epoch: 47 [0/20 (0%)] Loss: 0.576146 Train Epoch: 47 [2/20 (10%)] Loss: 0.501441 Train Epoch: 47 [4/20 (20%)] Loss: 0.612853 Train Epoch: 47 [6/20 (30%)] Loss: 0.627642 Train Epoch: 47 [8/20 (40%)] Loss: 0.546306 Train Epoch: 47 [10/20 (50%)] Loss: 0.543820 Train Epoch: 47 [12/20 (60%)] Loss: 0.694651 Train Epoch: 47 [14/20 (70%)] Loss: 0.497135 Train Epoch: 47 [16/20 (80%)] Loss: 0.600424 Train Epoch: 47 [18/20 (90%)] Loss: 0.494677 Test set: Avg. loss: 0.6732, Accuracy: 6351051/6599200 (96%)
New best! Saving... Train Epoch: 48 [0/20 (0%)] Loss: 0.584213 Train Epoch: 48 [2/20 (10%)] Loss: 0.702846 Train Epoch: 48 [4/20 (20%)] Loss: 0.582252 Train Epoch: 48 [6/20 (30%)] Loss: 0.580591 Train Epoch: 48 [8/20 (40%)] Loss: 0.568708 Train Epoch: 48 [10/20 (50%)] Loss: 0.618210 Train Epoch: 48 [12/20 (60%)] Loss: 0.545900 Train Epoch: 48 [14/20 (70%)] Loss: 0.452942 Train Epoch: 48 [16/20 (80%)] Loss: 0.569179 Train Epoch: 48 [18/20 (90%)] Loss: 0.432016 Test set: Avg. loss: 0.7579, Accuracy: 6376768/6599200 (97%)
Train Epoch: 49 [0/20 (0%)] Loss: 0.524290 Train Epoch: 49 [2/20 (10%)] Loss: 0.605775 Train Epoch: 49 [4/20 (20%)] Loss: 0.682340 Train Epoch: 49 [6/20 (30%)] Loss: 0.466730 Train Epoch: 49 [8/20 (40%)] Loss: 0.546996 Train Epoch: 49 [10/20 (50%)] Loss: 0.553756 Train Epoch: 49 [12/20 (60%)] Loss: 0.713644 Train Epoch: 49 [14/20 (70%)] Loss: 0.510814 Train Epoch: 49 [16/20 (80%)] Loss: 0.587497 Train Epoch: 49 [18/20 (90%)] Loss: 0.435607 Test set: Avg. loss: 0.6753, Accuracy: 6325177/6599200 (96%)
Train Epoch: 50 [0/20 (0%)] Loss: 0.478245 Train Epoch: 50 [2/20 (10%)] Loss: 0.651407 Train Epoch: 50 [4/20 (20%)] Loss: 0.766776 Train Epoch: 50 [6/20 (30%)] Loss: 0.440724 Train Epoch: 50 [8/20 (40%)] Loss: 0.571711 Train Epoch: 50 [10/20 (50%)] Loss: 0.541259 Train Epoch: 50 [12/20 (60%)] Loss: 0.564538 Train Epoch: 50 [14/20 (70%)] Loss: 0.584559 Train Epoch: 50 [16/20 (80%)] Loss: 0.428606 Train Epoch: 50 [18/20 (90%)] Loss: 0.550044 Test set: Avg. loss: 0.7491, Accuracy: 6364254/6599200 (96%)
Train Epoch: 51 [0/20 (0%)] Loss: 0.526194 Train Epoch: 51 [2/20 (10%)] Loss: 0.532228 Train Epoch: 51 [4/20 (20%)] Loss: 0.762404 Train Epoch: 51 [6/20 (30%)] Loss: 0.537093 Train Epoch: 51 [8/20 (40%)] Loss: 0.458473 Train Epoch: 51 [10/20 (50%)] Loss: 0.525459 Train Epoch: 51 [12/20 (60%)] Loss: 0.610049 Train Epoch: 51 [14/20 (70%)] Loss: 0.645488 Train Epoch: 51 [16/20 (80%)] Loss: 0.430296 Train Epoch: 51 [18/20 (90%)] Loss: 0.556412 Test set: Avg. loss: 0.7179, Accuracy: 6372126/6599200 (97%)
Train Epoch: 52 [0/20 (0%)] Loss: 0.465435 Train Epoch: 52 [2/20 (10%)] Loss: 0.450982 Train Epoch: 52 [4/20 (20%)] Loss: 0.645256 Train Epoch: 52 [6/20 (30%)] Loss: 0.592869 Train Epoch: 52 [8/20 (40%)] Loss: 0.606111 Train Epoch: 52 [10/20 (50%)] Loss: 0.538118 Train Epoch: 52 [12/20 (60%)] Loss: 0.633562 Train Epoch: 52 [14/20 (70%)] Loss: 0.586822 Train Epoch: 52 [16/20 (80%)] Loss: 0.572029 Train Epoch: 52 [18/20 (90%)] Loss: 0.516946 Test set: Avg. loss: 0.7663, Accuracy: 6358202/6599200 (96%)
Train Epoch: 53 [0/20 (0%)] Loss: 0.543512 Train Epoch: 53 [2/20 (10%)] Loss: 0.473704 Train Epoch: 53 [4/20 (20%)] Loss: 0.534407 Train Epoch: 53 [6/20 (30%)] Loss: 0.608285 Train Epoch: 53 [8/20 (40%)] Loss: 0.530789 Train Epoch: 53 [10/20 (50%)] Loss: 0.404254 Train Epoch: 53 [12/20 (60%)] Loss: 0.754913 Train Epoch: 53 [14/20 (70%)] Loss: 0.521855 Train Epoch: 53 [16/20 (80%)] Loss: 0.570267 Train Epoch: 53 [18/20 (90%)] Loss: 0.580097 Test set: Avg. loss: 0.7422, Accuracy: 6343546/6599200 (96%)
Train Epoch: 54 [0/20 (0%)] Loss: 0.464493 Train Epoch: 54 [2/20 (10%)] Loss: 0.465002 Train Epoch: 54 [4/20 (20%)] Loss: 0.555104 Train Epoch: 54 [6/20 (30%)] Loss: 0.607628 Train Epoch: 54 [8/20 (40%)] Loss: 0.497994 Train Epoch: 54 [10/20 (50%)] Loss: 0.516627 Train Epoch: 54 [12/20 (60%)] Loss: 0.573553 Train Epoch: 54 [14/20 (70%)] Loss: 0.567863 Train Epoch: 54 [16/20 (80%)] Loss: 0.663401 Train Epoch: 54 [18/20 (90%)] Loss: 0.604466 Test set: Avg. loss: 0.7268, Accuracy: 6355106/6599200 (96%)
Train Epoch: 55 [0/20 (0%)] Loss: 0.514372 Train Epoch: 55 [2/20 (10%)] Loss: 0.571956 Train Epoch: 55 [4/20 (20%)] Loss: 0.542222 Train Epoch: 55 [6/20 (30%)] Loss: 0.485010 Train Epoch: 55 [8/20 (40%)] Loss: 0.520358 Train Epoch: 55 [10/20 (50%)] Loss: 0.656401 Train Epoch: 55 [12/20 (60%)] Loss: 0.509497 Train Epoch: 55 [14/20 (70%)] Loss: 0.583786 Train Epoch: 55 [16/20 (80%)] Loss: 0.566324 Train Epoch: 55 [18/20 (90%)] Loss: 0.464446 Test set: Avg. loss: 0.7280, Accuracy: 6350273/6599200 (96%)
Train Epoch: 56 [0/20 (0%)] Loss: 0.595514 Train Epoch: 56 [2/20 (10%)] Loss: 0.565781 Train Epoch: 56 [4/20 (20%)] Loss: 0.539794 Train Epoch: 56 [6/20 (30%)] Loss: 0.496224 Train Epoch: 56 [8/20 (40%)] Loss: 0.558150 Train Epoch: 56 [10/20 (50%)] Loss: 0.466714 Train Epoch: 56 [12/20 (60%)] Loss: 0.593268 Train Epoch: 56 [14/20 (70%)] Loss: 0.458068 Train Epoch: 56 [16/20 (80%)] Loss: 0.536922 Train Epoch: 56 [18/20 (90%)] Loss: 0.564274 Test set: Avg. loss: 0.7920, Accuracy: 6367898/6599200 (96%)
Train Epoch: 57 [0/20 (0%)] Loss: 0.533619 Train Epoch: 57 [2/20 (10%)] Loss: 0.591627 Train Epoch: 57 [4/20 (20%)] Loss: 0.519929 Train Epoch: 57 [6/20 (30%)] Loss: 0.500502 Train Epoch: 57 [8/20 (40%)] Loss: 0.464531 Train Epoch: 57 [10/20 (50%)] Loss: 0.469356 Train Epoch: 57 [12/20 (60%)] Loss: 0.558189 Train Epoch: 57 [14/20 (70%)] Loss: 0.706595 Train Epoch: 57 [16/20 (80%)] Loss: 0.498012 Train Epoch: 57 [18/20 (90%)] Loss: 0.530095 Test set: Avg. loss: 0.7346, Accuracy: 6294684/6599200 (95%)
Train Epoch: 58 [0/20 (0%)] Loss: 0.589827 Train Epoch: 58 [2/20 (10%)] Loss: 0.570534 Train Epoch: 58 [4/20 (20%)] Loss: 0.492900 Train Epoch: 58 [6/20 (30%)] Loss: 0.520215 Train Epoch: 58 [8/20 (40%)] Loss: 0.520408 Train Epoch: 58 [10/20 (50%)] Loss: 0.542867 Train Epoch: 58 [12/20 (60%)] Loss: 0.611720 Train Epoch: 58 [14/20 (70%)] Loss: 0.555157 Train Epoch: 58 [16/20 (80%)] Loss: 0.473374 Train Epoch: 58 [18/20 (90%)] Loss: 0.419886 Test set: Avg. loss: 0.7391, Accuracy: 6340122/6599200 (96%)
Train Epoch: 59 [0/20 (0%)] Loss: 0.432145 Train Epoch: 59 [2/20 (10%)] Loss: 0.411104 Train Epoch: 59 [4/20 (20%)] Loss: 0.609574 Train Epoch: 59 [6/20 (30%)] Loss: 0.537748 Train Epoch: 59 [8/20 (40%)] Loss: 0.583100 Train Epoch: 59 [10/20 (50%)] Loss: 0.549934 Train Epoch: 59 [12/20 (60%)] Loss: 0.523164 Train Epoch: 59 [14/20 (70%)] Loss: 0.523137 Train Epoch: 59 [16/20 (80%)] Loss: 0.527451 Train Epoch: 59 [18/20 (90%)] Loss: 0.489548 Test set: Avg. loss: 0.8448, Accuracy: 6365492/6599200 (96%)
Train Epoch: 60 [0/20 (0%)] Loss: 0.519315 Train Epoch: 60 [2/20 (10%)] Loss: 0.484588 Train Epoch: 60 [4/20 (20%)] Loss: 0.441689 Train Epoch: 60 [6/20 (30%)] Loss: 0.607982 Train Epoch: 60 [8/20 (40%)] Loss: 0.490365 Train Epoch: 60 [10/20 (50%)] Loss: 0.515835 Train Epoch: 60 [12/20 (60%)] Loss: 0.569936 Train Epoch: 60 [14/20 (70%)] Loss: 0.542978 Train Epoch: 60 [16/20 (80%)] Loss: 0.463379 Train Epoch: 60 [18/20 (90%)] Loss: 0.467498 Test set: Avg. loss: 0.8933, Accuracy: 6353787/6599200 (96%)
Train Epoch: 61 [0/20 (0%)] Loss: 0.521879 Train Epoch: 61 [2/20 (10%)] Loss: 0.512570 Train Epoch: 61 [4/20 (20%)] Loss: 0.622087 Train Epoch: 61 [6/20 (30%)] Loss: 0.518844 Train Epoch: 61 [8/20 (40%)] Loss: 0.474172 Train Epoch: 61 [10/20 (50%)] Loss: 0.599370 Train Epoch: 61 [12/20 (60%)] Loss: 0.439452 Train Epoch: 61 [14/20 (70%)] Loss: 0.476481 Train Epoch: 61 [16/20 (80%)] Loss: 0.555010 Train Epoch: 61 [18/20 (90%)] Loss: 0.462507 Test set: Avg. loss: 0.8640, Accuracy: 6364112/6599200 (96%)
Train Epoch: 62 [0/20 (0%)] Loss: 0.572747 Train Epoch: 62 [2/20 (10%)] Loss: 0.528711 Train Epoch: 62 [4/20 (20%)] Loss: 0.414384 Train Epoch: 62 [6/20 (30%)] Loss: 0.587058 Train Epoch: 62 [8/20 (40%)] Loss: 0.585889 Train Epoch: 62 [10/20 (50%)] Loss: 0.454562 Train Epoch: 62 [12/20 (60%)] Loss: 0.507209 Train Epoch: 62 [14/20 (70%)] Loss: 0.486161 Train Epoch: 62 [16/20 (80%)] Loss: 0.478928 Train Epoch: 62 [18/20 (90%)] Loss: 0.544781 Test set: Avg. loss: 0.8868, Accuracy: 6368985/6599200 (97%)
Train Epoch: 63 [0/20 (0%)] Loss: 0.454590 Train Epoch: 63 [2/20 (10%)] Loss: 0.514680 Train Epoch: 63 [4/20 (20%)] Loss: 0.470097 Train Epoch: 63 [6/20 (30%)] Loss: 0.544657 Train Epoch: 63 [8/20 (40%)] Loss: 0.555274 Train Epoch: 63 [10/20 (50%)] Loss: 0.571414 Train Epoch: 63 [12/20 (60%)] Loss: 0.537948 Train Epoch: 63 [14/20 (70%)] Loss: 0.384695 Train Epoch: 63 [16/20 (80%)] Loss: 0.498066 Train Epoch: 63 [18/20 (90%)] Loss: 0.573439 Test set: Avg. loss: 0.7783, Accuracy: 6363281/6599200 (96%)
Train Epoch: 64 [0/20 (0%)] Loss: 0.549984 Train Epoch: 64 [2/20 (10%)] Loss: 0.581816 Train Epoch: 64 [4/20 (20%)] Loss: 0.507039 Train Epoch: 64 [6/20 (30%)] Loss: 0.412920 Train Epoch: 64 [8/20 (40%)] Loss: 0.431117 Train Epoch: 64 [10/20 (50%)] Loss: 0.443832 Train Epoch: 64 [12/20 (60%)] Loss: 0.488967 Train Epoch: 64 [14/20 (70%)] Loss: 0.604048 Train Epoch: 64 [16/20 (80%)] Loss: 0.458544 Train Epoch: 64 [18/20 (90%)] Loss: 0.539313 Test set: Avg. loss: 0.7414, Accuracy: 6312924/6599200 (96%)
Train Epoch: 65 [0/20 (0%)] Loss: 0.529650 Train Epoch: 65 [2/20 (10%)] Loss: 0.558923 Train Epoch: 65 [4/20 (20%)] Loss: 0.549753 Train Epoch: 65 [6/20 (30%)] Loss: 0.561704 Train Epoch: 65 [8/20 (40%)] Loss: 0.481002 Train Epoch: 65 [10/20 (50%)] Loss: 0.482808 Train Epoch: 65 [12/20 (60%)] Loss: 0.470785 Train Epoch: 65 [14/20 (70%)] Loss: 0.499392 Train Epoch: 65 [16/20 (80%)] Loss: 0.489587 Train Epoch: 65 [18/20 (90%)] Loss: 0.381255 Test set: Avg. loss: 0.7595, Accuracy: 6338083/6599200 (96%)
Train Epoch: 66 [0/20 (0%)] Loss: 0.481986 Train Epoch: 66 [2/20 (10%)] Loss: 0.550094 Train Epoch: 66 [4/20 (20%)] Loss: 0.445738 Train Epoch: 66 [6/20 (30%)] Loss: 0.448745 Train Epoch: 66 [8/20 (40%)] Loss: 0.516131 Train Epoch: 66 [10/20 (50%)] Loss: 0.422031 Train Epoch: 66 [12/20 (60%)] Loss: 0.515825 Train Epoch: 66 [14/20 (70%)] Loss: 0.532976 Train Epoch: 66 [16/20 (80%)] Loss: 0.559534 Train Epoch: 66 [18/20 (90%)] Loss: 0.386993 Test set: Avg. loss: 0.7581, Accuracy: 6298252/6599200 (95%)
Train Epoch: 67 [0/20 (0%)] Loss: 0.450697 Train Epoch: 67 [2/20 (10%)] Loss: 0.543733 Train Epoch: 67 [4/20 (20%)] Loss: 0.492970 Train Epoch: 67 [6/20 (30%)] Loss: 0.477428 Train Epoch: 67 [8/20 (40%)] Loss: 0.459787 Train Epoch: 67 [10/20 (50%)] Loss: 0.480734 Train Epoch: 67 [12/20 (60%)] Loss: 0.478733 Train Epoch: 67 [14/20 (70%)] Loss: 0.459753 Train Epoch: 67 [16/20 (80%)] Loss: 0.406543 Train Epoch: 67 [18/20 (90%)] Loss: 0.439672 Test set: Avg. loss: 0.8647, Accuracy: 6361236/6599200 (96%)
Train Epoch: 68 [0/20 (0%)] Loss: 0.553017 Train Epoch: 68 [2/20 (10%)] Loss: 0.492398 Train Epoch: 68 [4/20 (20%)] Loss: 0.444013 Train Epoch: 68 [6/20 (30%)] Loss: 0.493013 Train Epoch: 68 [8/20 (40%)] Loss: 0.389618 Train Epoch: 68 [10/20 (50%)] Loss: 0.506406 Train Epoch: 68 [12/20 (60%)] Loss: 0.405398 Train Epoch: 68 [14/20 (70%)] Loss: 0.387549 Train Epoch: 68 [16/20 (80%)] Loss: 0.452501 Train Epoch: 68 [18/20 (90%)] Loss: 0.481166 Test set: Avg. loss: 0.7935, Accuracy: 6340679/6599200 (96%)
Train Epoch: 69 [0/20 (0%)] Loss: 0.392621 Train Epoch: 69 [2/20 (10%)] Loss: 0.436303 Train Epoch: 69 [4/20 (20%)] Loss: 0.512869 Train Epoch: 69 [6/20 (30%)] Loss: 0.493828 Train Epoch: 69 [8/20 (40%)] Loss: 0.409064 Train Epoch: 69 [10/20 (50%)] Loss: 0.479633 Train Epoch: 69 [12/20 (60%)] Loss: 0.395969 Train Epoch: 69 [14/20 (70%)] Loss: 0.462093 Train Epoch: 69 [16/20 (80%)] Loss: 0.487615 Train Epoch: 69 [18/20 (90%)] Loss: 0.393348 Test set: Avg. loss: 0.9515, Accuracy: 6352319/6599200 (96%)
Train Epoch: 70 [0/20 (0%)] Loss: 0.406585 Train Epoch: 70 [2/20 (10%)] Loss: 0.463263 Train Epoch: 70 [4/20 (20%)] Loss: 0.459458 Train Epoch: 70 [6/20 (30%)] Loss: 0.425396 Train Epoch: 70 [8/20 (40%)] Loss: 0.393356 Train Epoch: 70 [10/20 (50%)] Loss: 0.496881 Train Epoch: 70 [12/20 (60%)] Loss: 0.501215 Train Epoch: 70 [14/20 (70%)] Loss: 0.457573 Train Epoch: 70 [16/20 (80%)] Loss: 0.432990 Train Epoch: 70 [18/20 (90%)] Loss: 0.406443 Test set: Avg. loss: 0.8949, Accuracy: 6354672/6599200 (96%)
Train Epoch: 71 [0/20 (0%)] Loss: 0.389619 Train Epoch: 71 [2/20 (10%)] Loss: 0.500392 Train Epoch: 71 [4/20 (20%)] Loss: 0.388166 Train Epoch: 71 [6/20 (30%)] Loss: 0.499961 Train Epoch: 71 [8/20 (40%)] Loss: 0.466328 Train Epoch: 71 [10/20 (50%)] Loss: 0.427787 Train Epoch: 71 [12/20 (60%)] Loss: 0.434250 Train Epoch: 71 [14/20 (70%)] Loss: 0.495321 Train Epoch: 71 [16/20 (80%)] Loss: 0.366330 Train Epoch: 71 [18/20 (90%)] Loss: 0.461780 Test set: Avg. loss: 0.9606, Accuracy: 6351072/6599200 (96%)
Train Epoch: 72 [0/20 (0%)] Loss: 0.441311 Train Epoch: 72 [2/20 (10%)] Loss: 0.360225 Train Epoch: 72 [4/20 (20%)] Loss: 0.467868 Train Epoch: 72 [6/20 (30%)] Loss: 0.451229 Train Epoch: 72 [8/20 (40%)] Loss: 0.439019 Train Epoch: 72 [10/20 (50%)] Loss: 0.444747 Train Epoch: 72 [12/20 (60%)] Loss: 0.480125 Train Epoch: 72 [14/20 (70%)] Loss: 0.427952 Train Epoch: 72 [16/20 (80%)] Loss: 0.391994 Train Epoch: 72 [18/20 (90%)] Loss: 0.453226 Test set: Avg. loss: 1.0002, Accuracy: 6364361/6599200 (96%)
Train Epoch: 73 [0/20 (0%)] Loss: 0.435905 Train Epoch: 73 [2/20 (10%)] Loss: 0.363961 Train Epoch: 73 [4/20 (20%)] Loss: 0.433254 Train Epoch: 73 [6/20 (30%)] Loss: 0.361045 Train Epoch: 73 [8/20 (40%)] Loss: 0.480288 Train Epoch: 73 [10/20 (50%)] Loss: 0.373983 Train Epoch: 73 [12/20 (60%)] Loss: 0.393451 Train Epoch: 73 [14/20 (70%)] Loss: 0.496771 Train Epoch: 73 [16/20 (80%)] Loss: 0.391041 Train Epoch: 73 [18/20 (90%)] Loss: 0.487613 Test set: Avg. loss: 0.8229, Accuracy: 6319404/6599200 (96%)
Train Epoch: 74 [0/20 (0%)] Loss: 0.432405 Train Epoch: 74 [2/20 (10%)] Loss: 0.367130 Train Epoch: 74 [4/20 (20%)] Loss: 0.325511 Train Epoch: 74 [6/20 (30%)] Loss: 0.413157 Train Epoch: 74 [8/20 (40%)] Loss: 0.427220 Train Epoch: 74 [10/20 (50%)] Loss: 0.506828 Train Epoch: 74 [12/20 (60%)] Loss: 0.451727 Train Epoch: 74 [14/20 (70%)] Loss: 0.429193 Train Epoch: 74 [16/20 (80%)] Loss: 0.407985 Train Epoch: 74 [18/20 (90%)] Loss: 0.398022 Test set: Avg. loss: 0.8534, Accuracy: 6298531/6599200 (95%)
Train Epoch: 75 [0/20 (0%)] Loss: 0.541968 Train Epoch: 75 [2/20 (10%)] Loss: 0.424038 Train Epoch: 75 [4/20 (20%)] Loss: 0.452679 Train Epoch: 75 [6/20 (30%)] Loss: 0.416729 Train Epoch: 75 [8/20 (40%)] Loss: 0.349599 Train Epoch: 75 [10/20 (50%)] Loss: 0.396300 Train Epoch: 75 [12/20 (60%)] Loss: 0.364884 Train Epoch: 75 [14/20 (70%)] Loss: 0.392056 Train Epoch: 75 [16/20 (80%)] Loss: 0.399034 Train Epoch: 75 [18/20 (90%)] Loss: 0.415208 Test set: Avg. loss: 0.9201, Accuracy: 6357869/6599200 (96%)
Train Epoch: 76 [0/20 (0%)] Loss: 0.417734 Train Epoch: 76 [2/20 (10%)] Loss: 0.437966 Train Epoch: 76 [4/20 (20%)] Loss: 0.368313 Train Epoch: 76 [6/20 (30%)] Loss: 0.392787 Train Epoch: 76 [8/20 (40%)] Loss: 0.457213 Train Epoch: 76 [10/20 (50%)] Loss: 0.429843 Train Epoch: 76 [12/20 (60%)] Loss: 0.396681 Train Epoch: 76 [14/20 (70%)] Loss: 0.372321 Train Epoch: 76 [16/20 (80%)] Loss: 0.363985 Train Epoch: 76 [18/20 (90%)] Loss: 0.387257 Test set: Avg. loss: 0.9951, Accuracy: 6356013/6599200 (96%)
Train Epoch: 77 [0/20 (0%)] Loss: 0.382521 Train Epoch: 77 [2/20 (10%)] Loss: 0.403188 Train Epoch: 77 [4/20 (20%)] Loss: 0.367487 Train Epoch: 77 [6/20 (30%)] Loss: 0.381525 Train Epoch: 77 [8/20 (40%)] Loss: 0.400769 Train Epoch: 77 [10/20 (50%)] Loss: 0.399856 Train Epoch: 77 [12/20 (60%)] Loss: 0.452680 Train Epoch: 77 [14/20 (70%)] Loss: 0.326946 Train Epoch: 77 [16/20 (80%)] Loss: 0.388051 Train Epoch: 77 [18/20 (90%)] Loss: 0.367500 Test set: Avg. loss: 0.9877, Accuracy: 6356859/6599200 (96%)
Train Epoch: 78 [0/20 (0%)] Loss: 0.329114 Train Epoch: 78 [2/20 (10%)] Loss: 0.310662 Train Epoch: 78 [4/20 (20%)] Loss: 0.365491 Train Epoch: 78 [6/20 (30%)] Loss: 0.361874 Train Epoch: 78 [8/20 (40%)] Loss: 0.398576 Train Epoch: 78 [10/20 (50%)] Loss: 0.428141 Train Epoch: 78 [12/20 (60%)] Loss: 0.414798 Train Epoch: 78 [14/20 (70%)] Loss: 0.399080 Train Epoch: 78 [16/20 (80%)] Loss: 0.399950 Train Epoch: 78 [18/20 (90%)] Loss: 0.319768 Test set: Avg. loss: 1.0651, Accuracy: 6350891/6599200 (96%)
Train Epoch: 79 [0/20 (0%)] Loss: 0.403253 Train Epoch: 79 [2/20 (10%)] Loss: 0.320739 Train Epoch: 79 [4/20 (20%)] Loss: 0.333830 Train Epoch: 79 [6/20 (30%)] Loss: 0.393768 Train Epoch: 79 [8/20 (40%)] Loss: 0.302319 Train Epoch: 79 [10/20 (50%)] Loss: 0.353252 Train Epoch: 79 [12/20 (60%)] Loss: 0.329532 Train Epoch: 79 [14/20 (70%)] Loss: 0.379167 Train Epoch: 79 [16/20 (80%)] Loss: 0.457386 Train Epoch: 79 [18/20 (90%)] Loss: 0.412659 Test set: Avg. loss: 0.9292, Accuracy: 6294023/6599200 (95%)
Train Epoch: 80 [0/20 (0%)] Loss: 0.373931 Train Epoch: 80 [2/20 (10%)] Loss: 0.390950 Train Epoch: 80 [4/20 (20%)] Loss: 0.383384 Train Epoch: 80 [6/20 (30%)] Loss: 0.333981 Train Epoch: 80 [8/20 (40%)] Loss: 0.431107 Train Epoch: 80 [10/20 (50%)] Loss: 0.373899 Train Epoch: 80 [12/20 (60%)] Loss: 0.314033 Train Epoch: 80 [14/20 (70%)] Loss: 0.364894 Train Epoch: 80 [16/20 (80%)] Loss: 0.326242 Train Epoch: 80 [18/20 (90%)] Loss: 0.378231 Test set: Avg. loss: 1.0101, Accuracy: 6363066/6599200 (96%)
Train Epoch: 81 [0/20 (0%)] Loss: 0.295356 Train Epoch: 81 [2/20 (10%)] Loss: 0.428347 Train Epoch: 81 [4/20 (20%)] Loss: 0.396830 Train Epoch: 81 [6/20 (30%)] Loss: 0.362395 Train Epoch: 81 [8/20 (40%)] Loss: 0.287595 Train Epoch: 81 [10/20 (50%)] Loss: 0.396143 Train Epoch: 81 [12/20 (60%)] Loss: 0.381883 Train Epoch: 81 [14/20 (70%)] Loss: 0.322813 Train Epoch: 81 [16/20 (80%)] Loss: 0.331274 Train Epoch: 81 [18/20 (90%)] Loss: 0.372708 Test set: Avg. loss: 1.0022, Accuracy: 6339230/6599200 (96%)
Train Epoch: 82 [0/20 (0%)] Loss: 0.379754 Train Epoch: 82 [2/20 (10%)] Loss: 0.338672 Train Epoch: 82 [4/20 (20%)] Loss: 0.296827 Train Epoch: 82 [6/20 (30%)] Loss: 0.388511 Train Epoch: 82 [8/20 (40%)] Loss: 0.342761 Train Epoch: 82 [10/20 (50%)] Loss: 0.385239 Train Epoch: 82 [12/20 (60%)] Loss: 0.362911 Train Epoch: 82 [14/20 (70%)] Loss: 0.440111 Train Epoch: 82 [16/20 (80%)] Loss: 0.317306 Train Epoch: 82 [18/20 (90%)] Loss: 0.369729 Test set: Avg. loss: 0.9449, Accuracy: 6320399/6599200 (96%)
Train Epoch: 83 [0/20 (0%)] Loss: 0.332422 Train Epoch: 83 [2/20 (10%)] Loss: 0.317532 Train Epoch: 83 [4/20 (20%)] Loss: 0.350476 Train Epoch: 83 [6/20 (30%)] Loss: 0.393943 Train Epoch: 83 [8/20 (40%)] Loss: 0.353452 Train Epoch: 83 [10/20 (50%)] Loss: 0.330974 Train Epoch: 83 [12/20 (60%)] Loss: 0.352982 Train Epoch: 83 [14/20 (70%)] Loss: 0.339368 Train Epoch: 83 [16/20 (80%)] Loss: 0.364109 Train Epoch: 83 [18/20 (90%)] Loss: 0.357792 Test set: Avg. loss: 1.1095, Accuracy: 6353937/6599200 (96%)
Train Epoch: 84 [0/20 (0%)] Loss: 0.349571 Train Epoch: 84 [2/20 (10%)] Loss: 0.326094 Train Epoch: 84 [4/20 (20%)] Loss: 0.319773 Train Epoch: 84 [6/20 (30%)] Loss: 0.407091 Train Epoch: 84 [8/20 (40%)] Loss: 0.297995 Train Epoch: 84 [10/20 (50%)] Loss: 0.312816 Train Epoch: 84 [12/20 (60%)] Loss: 0.379650 Train Epoch: 84 [14/20 (70%)] Loss: 0.352885 Train Epoch: 84 [16/20 (80%)] Loss: 0.293235 Train Epoch: 84 [18/20 (90%)] Loss: 0.300639 Test set: Avg. loss: 1.0397, Accuracy: 6357688/6599200 (96%)
Train Epoch: 85 [0/20 (0%)] Loss: 0.338250 Train Epoch: 85 [2/20 (10%)] Loss: 0.336331 Train Epoch: 85 [4/20 (20%)] Loss: 0.316279 Train Epoch: 85 [6/20 (30%)] Loss: 0.303230 Train Epoch: 85 [8/20 (40%)] Loss: 0.341531 Train Epoch: 85 [10/20 (50%)] Loss: 0.277982 Train Epoch: 85 [12/20 (60%)] Loss: 0.350776 Train Epoch: 85 [14/20 (70%)] Loss: 0.286028 Train Epoch: 85 [16/20 (80%)] Loss: 0.289819 Train Epoch: 85 [18/20 (90%)] Loss: 0.328378 Test set: Avg. loss: 1.1644, Accuracy: 6356906/6599200 (96%)
Train Epoch: 86 [0/20 (0%)] Loss: 0.291324 Train Epoch: 86 [2/20 (10%)] Loss: 0.308537 Train Epoch: 86 [4/20 (20%)] Loss: 0.282726 Train Epoch: 86 [6/20 (30%)] Loss: 0.297520 Train Epoch: 86 [8/20 (40%)] Loss: 0.334882 Train Epoch: 86 [10/20 (50%)] Loss: 0.252935 Train Epoch: 86 [12/20 (60%)] Loss: 0.278223 Train Epoch: 86 [14/20 (70%)] Loss: 0.316239 Train Epoch: 86 [16/20 (80%)] Loss: 0.310915 Train Epoch: 86 [18/20 (90%)] Loss: 0.326022 Test set: Avg. loss: 1.1285, Accuracy: 6352216/6599200 (96%)
Train Epoch: 87 [0/20 (0%)] Loss: 0.258681 Train Epoch: 87 [2/20 (10%)] Loss: 0.318674 Train Epoch: 87 [4/20 (20%)] Loss: 0.252910 Train Epoch: 87 [6/20 (30%)] Loss: 0.313393 Train Epoch: 87 [8/20 (40%)] Loss: 0.297404 Train Epoch: 87 [10/20 (50%)] Loss: 0.268100 Train Epoch: 87 [12/20 (60%)] Loss: 0.328687 Train Epoch: 87 [14/20 (70%)] Loss: 0.355895 Train Epoch: 87 [16/20 (80%)] Loss: 0.246667 Train Epoch: 87 [18/20 (90%)] Loss: 0.314764 Test set: Avg. loss: 1.1227, Accuracy: 6348719/6599200 (96%)
Train Epoch: 88 [0/20 (0%)] Loss: 0.256977 Train Epoch: 88 [2/20 (10%)] Loss: 0.288261 Train Epoch: 88 [4/20 (20%)] Loss: 0.286548 Train Epoch: 88 [6/20 (30%)] Loss: 0.292538 Train Epoch: 88 [8/20 (40%)] Loss: 0.377225 Train Epoch: 88 [10/20 (50%)] Loss: 0.242427 Train Epoch: 88 [12/20 (60%)] Loss: 0.311031 Train Epoch: 88 [14/20 (70%)] Loss: 0.321700 Train Epoch: 88 [16/20 (80%)] Loss: 0.299956 Train Epoch: 88 [18/20 (90%)] Loss: 0.311418 Test set: Avg. loss: 1.1708, Accuracy: 6352110/6599200 (96%)
Train Epoch: 89 [0/20 (0%)] Loss: 0.225906 Train Epoch: 89 [2/20 (10%)] Loss: 0.268573 Train Epoch: 89 [4/20 (20%)] Loss: 0.319467 Train Epoch: 89 [6/20 (30%)] Loss: 0.290661 Train Epoch: 89 [8/20 (40%)] Loss: 0.310296 Train Epoch: 89 [10/20 (50%)] Loss: 0.245470 Train Epoch: 89 [12/20 (60%)] Loss: 0.296386 Train Epoch: 89 [14/20 (70%)] Loss: 0.339619 Train Epoch: 89 [16/20 (80%)] Loss: 0.278782 Train Epoch: 89 [18/20 (90%)] Loss: 0.274957 Test set: Avg. loss: 1.1429, Accuracy: 6348034/6599200 (96%)
Train Epoch: 90 [0/20 (0%)] Loss: 0.263932 Train Epoch: 90 [2/20 (10%)] Loss: 0.276351 Train Epoch: 90 [4/20 (20%)] Loss: 0.223623 Train Epoch: 90 [6/20 (30%)] Loss: 0.290620 Train Epoch: 90 [8/20 (40%)] Loss: 0.239828 Train Epoch: 90 [10/20 (50%)] Loss: 0.306629 Train Epoch: 90 [12/20 (60%)] Loss: 0.333290 Train Epoch: 90 [14/20 (70%)] Loss: 0.241686 Train Epoch: 90 [16/20 (80%)] Loss: 0.292463 Train Epoch: 90 [18/20 (90%)] Loss: 0.293783 Test set: Avg. loss: 1.1369, Accuracy: 6329907/6599200 (96%)
Train Epoch: 91 [0/20 (0%)] Loss: 0.297992 Train Epoch: 91 [2/20 (10%)] Loss: 0.279793 Train Epoch: 91 [4/20 (20%)] Loss: 0.220262 Train Epoch: 91 [6/20 (30%)] Loss: 0.268158 Train Epoch: 91 [8/20 (40%)] Loss: 0.264984 Train Epoch: 91 [10/20 (50%)] Loss: 0.271016 Train Epoch: 91 [12/20 (60%)] Loss: 0.257479 Train Epoch: 91 [14/20 (70%)] Loss: 0.272015 Train Epoch: 91 [16/20 (80%)] Loss: 0.250125 Train Epoch: 91 [18/20 (90%)] Loss: 0.292273 Test set: Avg. loss: 1.1533, Accuracy: 6336367/6599200 (96%)
Train Epoch: 92 [0/20 (0%)] Loss: 0.292546 Train Epoch: 92 [2/20 (10%)] Loss: 0.264167 Train Epoch: 92 [4/20 (20%)] Loss: 0.242992 Train Epoch: 92 [6/20 (30%)] Loss: 0.271265 Train Epoch: 92 [8/20 (40%)] Loss: 0.219122 Train Epoch: 92 [10/20 (50%)] Loss: 0.236394 Train Epoch: 92 [12/20 (60%)] Loss: 0.268104 Train Epoch: 92 [14/20 (70%)] Loss: 0.269465 Train Epoch: 92 [16/20 (80%)] Loss: 0.227100 Train Epoch: 92 [18/20 (90%)] Loss: 0.243773 Test set: Avg. loss: 1.3263, Accuracy: 6359639/6599200 (96%)
Train Epoch: 93 [0/20 (0%)] Loss: 0.254834 Train Epoch: 93 [2/20 (10%)] Loss: 0.217824 Train Epoch: 93 [4/20 (20%)] Loss: 0.263309 Train Epoch: 93 [6/20 (30%)] Loss: 0.229319 Train Epoch: 93 [8/20 (40%)] Loss: 0.253264 Train Epoch: 93 [10/20 (50%)] Loss: 0.268864 Train Epoch: 93 [12/20 (60%)] Loss: 0.260978 Train Epoch: 93 [14/20 (70%)] Loss: 0.271566 Train Epoch: 93 [16/20 (80%)] Loss: 0.261888 Train Epoch: 93 [18/20 (90%)] Loss: 0.230490 Test set: Avg. loss: 1.2445, Accuracy: 6346717/6599200 (96%)
Train Epoch: 94 [0/20 (0%)] Loss: 0.246707 Train Epoch: 94 [2/20 (10%)] Loss: 0.227001 Train Epoch: 94 [4/20 (20%)] Loss: 0.205742 Train Epoch: 94 [6/20 (30%)] Loss: 0.217912 Train Epoch: 94 [8/20 (40%)] Loss: 0.252945 Train Epoch: 94 [10/20 (50%)] Loss: 0.225995 Train Epoch: 94 [12/20 (60%)] Loss: 0.229490 Train Epoch: 94 [14/20 (70%)] Loss: 0.345120 Train Epoch: 94 [16/20 (80%)] Loss: 0.252028 Train Epoch: 94 [18/20 (90%)] Loss: 0.259729 Test set: Avg. loss: 1.2229, Accuracy: 6332331/6599200 (96%)
Train Epoch: 95 [0/20 (0%)] Loss: 0.248836 Train Epoch: 95 [2/20 (10%)] Loss: 0.199592 Train Epoch: 95 [4/20 (20%)] Loss: 0.255101 Train Epoch: 95 [6/20 (30%)] Loss: 0.238518 Train Epoch: 95 [8/20 (40%)] Loss: 0.259458 Train Epoch: 95 [10/20 (50%)] Loss: 0.270610 Train Epoch: 95 [12/20 (60%)] Loss: 0.261913 Train Epoch: 95 [14/20 (70%)] Loss: 0.249989 Train Epoch: 95 [16/20 (80%)] Loss: 0.206504 Train Epoch: 95 [18/20 (90%)] Loss: 0.208458 Test set: Avg. loss: 1.4121, Accuracy: 6356907/6599200 (96%)
Train Epoch: 96 [0/20 (0%)] Loss: 0.315180 Train Epoch: 96 [2/20 (10%)] Loss: 0.225335 Train Epoch: 96 [4/20 (20%)] Loss: 0.213083 Train Epoch: 96 [6/20 (30%)] Loss: 0.226962 Train Epoch: 96 [8/20 (40%)] Loss: 0.226390 Train Epoch: 96 [10/20 (50%)] Loss: 0.247425 Train Epoch: 96 [12/20 (60%)] Loss: 0.238043 Train Epoch: 96 [14/20 (70%)] Loss: 0.205429 Train Epoch: 96 [16/20 (80%)] Loss: 0.232802 Train Epoch: 96 [18/20 (90%)] Loss: 0.256491 Test set: Avg. loss: 1.2563, Accuracy: 6332028/6599200 (96%)
Train Epoch: 97 [0/20 (0%)] Loss: 0.208965 Train Epoch: 97 [2/20 (10%)] Loss: 0.228817 Train Epoch: 97 [4/20 (20%)] Loss: 0.206115 Train Epoch: 97 [6/20 (30%)] Loss: 0.213995 Train Epoch: 97 [8/20 (40%)] Loss: 0.229663 Train Epoch: 97 [10/20 (50%)] Loss: 0.203070 Train Epoch: 97 [12/20 (60%)] Loss: 0.234141 Train Epoch: 97 [14/20 (70%)] Loss: 0.203738 Train Epoch: 97 [16/20 (80%)] Loss: 0.196475 Train Epoch: 97 [18/20 (90%)] Loss: 0.284918 Test set: Avg. loss: 1.2539, Accuracy: 6344170/6599200 (96%)
Train Epoch: 98 [0/20 (0%)] Loss: 0.194997 Train Epoch: 98 [2/20 (10%)] Loss: 0.192695 Train Epoch: 98 [4/20 (20%)] Loss: 0.238363 Train Epoch: 98 [6/20 (30%)] Loss: 0.214801 Train Epoch: 98 [8/20 (40%)] Loss: 0.216634 Train Epoch: 98 [10/20 (50%)] Loss: 0.214577 Train Epoch: 98 [12/20 (60%)] Loss: 0.189317 Train Epoch: 98 [14/20 (70%)] Loss: 0.241863 Train Epoch: 98 [16/20 (80%)] Loss: 0.241789 Train Epoch: 98 [18/20 (90%)] Loss: 0.207903 Test set: Avg. loss: 1.4084, Accuracy: 6350645/6599200 (96%)
Train Epoch: 99 [0/20 (0%)] Loss: 0.258651 Train Epoch: 99 [2/20 (10%)] Loss: 0.218139 Train Epoch: 99 [4/20 (20%)] Loss: 0.236042 Train Epoch: 99 [6/20 (30%)] Loss: 0.207573 Train Epoch: 99 [8/20 (40%)] Loss: 0.213724 Train Epoch: 99 [10/20 (50%)] Loss: 0.206228 Train Epoch: 99 [12/20 (60%)] Loss: 0.166391 Train Epoch: 99 [14/20 (70%)] Loss: 0.242016 Train Epoch: 99 [16/20 (80%)] Loss: 0.230063 Train Epoch: 99 [18/20 (90%)] Loss: 0.195098 Test set: Avg. loss: 1.2740, Accuracy: 6355357/6599200 (96%)
Train Epoch: 100 [0/20 (0%)] Loss: 0.198204 Train Epoch: 100 [2/20 (10%)] Loss: 0.240696 Train Epoch: 100 [4/20 (20%)] Loss: 0.203599 Train Epoch: 100 [6/20 (30%)] Loss: 0.197064 Train Epoch: 100 [8/20 (40%)] Loss: 0.222571 Train Epoch: 100 [10/20 (50%)] Loss: 0.210420 Train Epoch: 100 [12/20 (60%)] Loss: 0.193138 Train Epoch: 100 [14/20 (70%)] Loss: 0.215858 Train Epoch: 100 [16/20 (80%)] Loss: 0.154704 Train Epoch: 100 [18/20 (90%)] Loss: 0.223572 Test set: Avg. loss: 1.3759, Accuracy: 6354474/6599200 (96%)
Train Epoch: 101 [0/20 (0%)] Loss: 0.194340 Train Epoch: 101 [2/20 (10%)] Loss: 0.181810 Train Epoch: 101 [4/20 (20%)] Loss: 0.186213 Train Epoch: 101 [6/20 (30%)] Loss: 0.164224 Train Epoch: 101 [8/20 (40%)] Loss: 0.205567 Train Epoch: 101 [10/20 (50%)] Loss: 0.134715 Train Epoch: 101 [12/20 (60%)] Loss: 0.190660 Train Epoch: 101 [14/20 (70%)] Loss: 0.165756 Train Epoch: 101 [16/20 (80%)] Loss: 0.193772 Train Epoch: 101 [18/20 (90%)] Loss: 0.163569 Test set: Avg. loss: 1.4660, Accuracy: 6360639/6599200 (96%)
Train Epoch: 102 [0/20 (0%)] Loss: 0.165571 Train Epoch: 102 [2/20 (10%)] Loss: 0.179280 Train Epoch: 102 [4/20 (20%)] Loss: 0.164329 Train Epoch: 102 [6/20 (30%)] Loss: 0.145014 Train Epoch: 102 [8/20 (40%)] Loss: 0.161539 Train Epoch: 102 [10/20 (50%)] Loss: 0.153656 Train Epoch: 102 [12/20 (60%)] Loss: 0.144859 Train Epoch: 102 [14/20 (70%)] Loss: 0.146386 Train Epoch: 102 [16/20 (80%)] Loss: 0.166854 Train Epoch: 102 [18/20 (90%)] Loss: 0.146554 Test set: Avg. loss: 1.4970, Accuracy: 6364380/6599200 (96%)
Train Epoch: 103 [0/20 (0%)] Loss: 0.141317 Train Epoch: 103 [2/20 (10%)] Loss: 0.142475 Train Epoch: 103 [4/20 (20%)] Loss: 0.166465 Train Epoch: 103 [6/20 (30%)] Loss: 0.141911 Train Epoch: 103 [8/20 (40%)] Loss: 0.145245 Train Epoch: 103 [10/20 (50%)] Loss: 0.135992 Train Epoch: 103 [12/20 (60%)] Loss: 0.135754 Train Epoch: 103 [14/20 (70%)] Loss: 0.166058 Train Epoch: 103 [16/20 (80%)] Loss: 0.131382 Train Epoch: 103 [18/20 (90%)] Loss: 0.123569 Test set: Avg. loss: 1.5739, Accuracy: 6362133/6599200 (96%)
Train Epoch: 104 [0/20 (0%)] Loss: 0.112709 Train Epoch: 104 [2/20 (10%)] Loss: 0.128071 Train Epoch: 104 [4/20 (20%)] Loss: 0.157645 Train Epoch: 104 [6/20 (30%)] Loss: 0.124649 Train Epoch: 104 [8/20 (40%)] Loss: 0.136647 Train Epoch: 104 [10/20 (50%)] Loss: 0.136271 Train Epoch: 104 [12/20 (60%)] Loss: 0.161012 Train Epoch: 104 [14/20 (70%)] Loss: 0.129126 Train Epoch: 104 [16/20 (80%)] Loss: 0.115580 Train Epoch: 104 [18/20 (90%)] Loss: 0.108564 Test set: Avg. loss: 1.6311, Accuracy: 6360254/6599200 (96%)
Train Epoch: 105 [0/20 (0%)] Loss: 0.111832 Train Epoch: 105 [2/20 (10%)] Loss: 0.134523 Train Epoch: 105 [4/20 (20%)] Loss: 0.106472 Train Epoch: 105 [6/20 (30%)] Loss: 0.139867 Train Epoch: 105 [8/20 (40%)] Loss: 0.156243 Train Epoch: 105 [10/20 (50%)] Loss: 0.122042 Train Epoch: 105 [12/20 (60%)] Loss: 0.128117 Train Epoch: 105 [14/20 (70%)] Loss: 0.139671 Train Epoch: 105 [16/20 (80%)] Loss: 0.136717 Train Epoch: 105 [18/20 (90%)] Loss: 0.114743 Test set: Avg. loss: 1.6185, Accuracy: 6359003/6599200 (96%)
Train Epoch: 106 [0/20 (0%)] Loss: 0.113773 Train Epoch: 106 [2/20 (10%)] Loss: 0.116052 Train Epoch: 106 [4/20 (20%)] Loss: 0.108066 Train Epoch: 106 [6/20 (30%)] Loss: 0.119882 Train Epoch: 106 [8/20 (40%)] Loss: 0.118734 Train Epoch: 106 [10/20 (50%)] Loss: 0.142916 Train Epoch: 106 [12/20 (60%)] Loss: 0.134358 Train Epoch: 106 [14/20 (70%)] Loss: 0.112267 Train Epoch: 106 [16/20 (80%)] Loss: 0.117762 Train Epoch: 106 [18/20 (90%)] Loss: 0.149562 Test set: Avg. loss: 1.7542, Accuracy: 6361028/6599200 (96%)
Train Epoch: 107 [0/20 (0%)] Loss: 0.131805 Train Epoch: 107 [2/20 (10%)] Loss: 0.089512 Train Epoch: 107 [4/20 (20%)] Loss: 0.153884 Train Epoch: 107 [6/20 (30%)] Loss: 0.104724 Train Epoch: 107 [8/20 (40%)] Loss: 0.140175 Train Epoch: 107 [10/20 (50%)] Loss: 0.131110 Train Epoch: 107 [12/20 (60%)] Loss: 0.098150 Train Epoch: 107 [14/20 (70%)] Loss: 0.107798 Train Epoch: 107 [16/20 (80%)] Loss: 0.139323 Train Epoch: 107 [18/20 (90%)] Loss: 0.108347 Test set: Avg. loss: 1.6668, Accuracy: 6357669/6599200 (96%)
Train Epoch: 108 [0/20 (0%)] Loss: 0.114829 Train Epoch: 108 [2/20 (10%)] Loss: 0.129941 Train Epoch: 108 [4/20 (20%)] Loss: 0.135655 Train Epoch: 108 [6/20 (30%)] Loss: 0.133234 Train Epoch: 108 [8/20 (40%)] Loss: 0.112114 Train Epoch: 108 [10/20 (50%)] Loss: 0.109861 Train Epoch: 108 [12/20 (60%)] Loss: 0.127128 Train Epoch: 108 [14/20 (70%)] Loss: 0.093457 Train Epoch: 108 [16/20 (80%)] Loss: 0.124869 Train Epoch: 108 [18/20 (90%)] Loss: 0.103214 Test set: Avg. loss: 1.7182, Accuracy: 6360220/6599200 (96%)
Train Epoch: 109 [0/20 (0%)] Loss: 0.095574 Train Epoch: 109 [2/20 (10%)] Loss: 0.109762 Train Epoch: 109 [4/20 (20%)] Loss: 0.109087 Train Epoch: 109 [6/20 (30%)] Loss: 0.088645 Train Epoch: 109 [8/20 (40%)] Loss: 0.121944 Train Epoch: 109 [10/20 (50%)] Loss: 0.103997 Train Epoch: 109 [12/20 (60%)] Loss: 0.120194 Train Epoch: 109 [14/20 (70%)] Loss: 0.106712 Train Epoch: 109 [16/20 (80%)] Loss: 0.130401 Train Epoch: 109 [18/20 (90%)] Loss: 0.134792 Test set: Avg. loss: 1.7719, Accuracy: 6360288/6599200 (96%)
Train Epoch: 110 [0/20 (0%)] Loss: 0.079844 Train Epoch: 110 [2/20 (10%)] Loss: 0.106157 Train Epoch: 110 [4/20 (20%)] Loss: 0.103110 Train Epoch: 110 [6/20 (30%)] Loss: 0.097820 Train Epoch: 110 [8/20 (40%)] Loss: 0.097074 Train Epoch: 110 [10/20 (50%)] Loss: 0.110471 Train Epoch: 110 [12/20 (60%)] Loss: 0.100118 Train Epoch: 110 [14/20 (70%)] Loss: 0.092498 Train Epoch: 110 [16/20 (80%)] Loss: 0.110408 Train Epoch: 110 [18/20 (90%)] Loss: 0.114444 Test set: Avg. loss: 1.8018, Accuracy: 6361731/6599200 (96%)
Train Epoch: 111 [0/20 (0%)] Loss: 0.099743 Train Epoch: 111 [2/20 (10%)] Loss: 0.114934 Train Epoch: 111 [4/20 (20%)] Loss: 0.080817 Train Epoch: 111 [6/20 (30%)] Loss: 0.091930 Train Epoch: 111 [8/20 (40%)] Loss: 0.092161 Train Epoch: 111 [10/20 (50%)] Loss: 0.109547 Train Epoch: 111 [12/20 (60%)] Loss: 0.094544 Train Epoch: 111 [14/20 (70%)] Loss: 0.102456 Train Epoch: 111 [16/20 (80%)] Loss: 0.100024 Train Epoch: 111 [18/20 (90%)] Loss: 0.099622 Test set: Avg. loss: 1.7904, Accuracy: 6358698/6599200 (96%)
Train Epoch: 112 [0/20 (0%)] Loss: 0.118326 Train Epoch: 112 [2/20 (10%)] Loss: 0.091098 Train Epoch: 112 [4/20 (20%)] Loss: 0.097229 Train Epoch: 112 [6/20 (30%)] Loss: 0.080550 Train Epoch: 112 [8/20 (40%)] Loss: 0.093245 Train Epoch: 112 [10/20 (50%)] Loss: 0.086621 Train Epoch: 112 [12/20 (60%)] Loss: 0.146811 Train Epoch: 112 [14/20 (70%)] Loss: 0.133566 Train Epoch: 112 [16/20 (80%)] Loss: 0.098249 Train Epoch: 112 [18/20 (90%)] Loss: 0.092355 Test set: Avg. loss: 1.7944, Accuracy: 6354986/6599200 (96%)
Train Epoch: 113 [0/20 (0%)] Loss: 0.109371 Train Epoch: 113 [2/20 (10%)] Loss: 0.112374 Train Epoch: 113 [4/20 (20%)] Loss: 0.101541 Train Epoch: 113 [6/20 (30%)] Loss: 0.093689 Train Epoch: 113 [8/20 (40%)] Loss: 0.078705 Train Epoch: 113 [10/20 (50%)] Loss: 0.099341 Train Epoch: 113 [12/20 (60%)] Loss: 0.105123 Train Epoch: 113 [14/20 (70%)] Loss: 0.095020 Train Epoch: 113 [16/20 (80%)] Loss: 0.115681 Train Epoch: 113 [18/20 (90%)] Loss: 0.118352 Test set: Avg. loss: 1.8562, Accuracy: 6359600/6599200 (96%)
Train Epoch: 114 [0/20 (0%)] Loss: 0.098349 Train Epoch: 114 [2/20 (10%)] Loss: 0.086099 Train Epoch: 114 [4/20 (20%)] Loss: 0.114491 Train Epoch: 114 [6/20 (30%)] Loss: 0.094812 Train Epoch: 114 [8/20 (40%)] Loss: 0.100819 Train Epoch: 114 [10/20 (50%)] Loss: 0.095936 Train Epoch: 114 [12/20 (60%)] Loss: 0.092740 Train Epoch: 114 [14/20 (70%)] Loss: 0.128102 Train Epoch: 114 [16/20 (80%)] Loss: 0.095450 Train Epoch: 114 [18/20 (90%)] Loss: 0.106209 Test set: Avg. loss: 1.8392, Accuracy: 6353300/6599200 (96%)
Train Epoch: 115 [0/20 (0%)] Loss: 0.088118 Train Epoch: 115 [2/20 (10%)] Loss: 0.109605 Train Epoch: 115 [4/20 (20%)] Loss: 0.094387 Train Epoch: 115 [6/20 (30%)] Loss: 0.124669 Train Epoch: 115 [8/20 (40%)] Loss: 0.105346 Train Epoch: 115 [10/20 (50%)] Loss: 0.093897 Train Epoch: 115 [12/20 (60%)] Loss: 0.084217 Train Epoch: 115 [14/20 (70%)] Loss: 0.118438 Train Epoch: 115 [16/20 (80%)] Loss: 0.118948 Train Epoch: 115 [18/20 (90%)] Loss: 0.090886 Test set: Avg. loss: 1.8288, Accuracy: 6354735/6599200 (96%)
Train Epoch: 116 [0/20 (0%)] Loss: 0.099193 Train Epoch: 116 [2/20 (10%)] Loss: 0.087172 Train Epoch: 116 [4/20 (20%)] Loss: 0.089103 Train Epoch: 116 [6/20 (30%)] Loss: 0.105622 Train Epoch: 116 [8/20 (40%)] Loss: 0.124452 Train Epoch: 116 [10/20 (50%)] Loss: 0.087612 Train Epoch: 116 [12/20 (60%)] Loss: 0.117656 Train Epoch: 116 [14/20 (70%)] Loss: 0.128191 Train Epoch: 116 [16/20 (80%)] Loss: 0.096892 Train Epoch: 116 [18/20 (90%)] Loss: 0.107063 Test set: Avg. loss: 1.9760, Accuracy: 6354565/6599200 (96%)
Train Epoch: 117 [0/20 (0%)] Loss: 0.087535 Train Epoch: 117 [2/20 (10%)] Loss: 0.087741 Train Epoch: 117 [4/20 (20%)] Loss: 0.103393 Train Epoch: 117 [6/20 (30%)] Loss: 0.126086 Train Epoch: 117 [8/20 (40%)] Loss: 0.119488 Train Epoch: 117 [10/20 (50%)] Loss: 0.105676 Train Epoch: 117 [12/20 (60%)] Loss: 0.098200 Train Epoch: 117 [14/20 (70%)] Loss: 0.089204 Train Epoch: 117 [16/20 (80%)] Loss: 0.144709 Train Epoch: 117 [18/20 (90%)] Loss: 0.108557 Test set: Avg. loss: 1.9437, Accuracy: 6357608/6599200 (96%)
Train Epoch: 118 [0/20 (0%)] Loss: 0.109933 Train Epoch: 118 [2/20 (10%)] Loss: 0.109705 Train Epoch: 118 [4/20 (20%)] Loss: 0.098649 Train Epoch: 118 [6/20 (30%)] Loss: 0.124845 Train Epoch: 118 [8/20 (40%)] Loss: 0.109057 Train Epoch: 118 [10/20 (50%)] Loss: 0.099944 Train Epoch: 118 [12/20 (60%)] Loss: 0.137574 Train Epoch: 118 [14/20 (70%)] Loss: 0.146806 Train Epoch: 118 [16/20 (80%)] Loss: 0.104555 Train Epoch: 118 [18/20 (90%)] Loss: 0.123667 Test set: Avg. loss: 1.7896, Accuracy: 6346930/6599200 (96%)
Train Epoch: 119 [0/20 (0%)] Loss: 0.093918 Train Epoch: 119 [2/20 (10%)] Loss: 0.098558 Train Epoch: 119 [4/20 (20%)] Loss: 0.141596 Train Epoch: 119 [6/20 (30%)] Loss: 0.109769 Train Epoch: 119 [8/20 (40%)] Loss: 0.099081 Train Epoch: 119 [10/20 (50%)] Loss: 0.125525 Train Epoch: 119 [12/20 (60%)] Loss: 0.126666 Train Epoch: 119 [14/20 (70%)] Loss: 0.124266 Train Epoch: 119 [16/20 (80%)] Loss: 0.124688 Train Epoch: 119 [18/20 (90%)] Loss: 0.093543 Test set: Avg. loss: 1.8166, Accuracy: 6352529/6599200 (96%)
Train Epoch: 120 [0/20 (0%)] Loss: 0.105276 Train Epoch: 120 [2/20 (10%)] Loss: 0.091302 Train Epoch: 120 [4/20 (20%)] Loss: 0.089276 Train Epoch: 120 [6/20 (30%)] Loss: 0.104437 Train Epoch: 120 [8/20 (40%)] Loss: 0.108849 Train Epoch: 120 [10/20 (50%)] Loss: 0.093308 Train Epoch: 120 [12/20 (60%)] Loss: 0.074018 Train Epoch: 120 [14/20 (70%)] Loss: 0.123759 Train Epoch: 120 [16/20 (80%)] Loss: 0.106243 Train Epoch: 120 [18/20 (90%)] Loss: 0.126473 Test set: Avg. loss: 1.8251, Accuracy: 6352132/6599200 (96%)
Train Epoch: 121 [0/20 (0%)] Loss: 0.107419 Train Epoch: 121 [2/20 (10%)] Loss: 0.091434 Train Epoch: 121 [4/20 (20%)] Loss: 0.112277 Train Epoch: 121 [6/20 (30%)] Loss: 0.080574 Train Epoch: 121 [8/20 (40%)] Loss: 0.075231 Train Epoch: 121 [10/20 (50%)] Loss: 0.111269 Train Epoch: 121 [12/20 (60%)] Loss: 0.100841 Train Epoch: 121 [14/20 (70%)] Loss: 0.125489 Train Epoch: 121 [16/20 (80%)] Loss: 0.088435 Train Epoch: 121 [18/20 (90%)] Loss: 0.098353 Test set: Avg. loss: 1.8890, Accuracy: 6357794/6599200 (96%)
Train Epoch: 122 [0/20 (0%)] Loss: 0.079131 Train Epoch: 122 [2/20 (10%)] Loss: 0.097631 Train Epoch: 122 [4/20 (20%)] Loss: 0.074461 Train Epoch: 122 [6/20 (30%)] Loss: 0.112556 Train Epoch: 122 [8/20 (40%)] Loss: 0.081599 Train Epoch: 122 [10/20 (50%)] Loss: 0.112352 Train Epoch: 122 [12/20 (60%)] Loss: 0.085404 Train Epoch: 122 [14/20 (70%)] Loss: 0.094233 Train Epoch: 122 [16/20 (80%)] Loss: 0.085921 Train Epoch: 122 [18/20 (90%)] Loss: 0.074676 Test set: Avg. loss: 1.8879, Accuracy: 6360918/6599200 (96%)
Train Epoch: 123 [0/20 (0%)] Loss: 0.088056 Train Epoch: 123 [2/20 (10%)] Loss: 0.077280 Train Epoch: 123 [4/20 (20%)] Loss: 0.073809 Train Epoch: 123 [6/20 (30%)] Loss: 0.084730 Train Epoch: 123 [8/20 (40%)] Loss: 0.080499 Train Epoch: 123 [10/20 (50%)] Loss: 0.079129 Train Epoch: 123 [12/20 (60%)] Loss: 0.081046 Train Epoch: 123 [14/20 (70%)] Loss: 0.069508 Train Epoch: 123 [16/20 (80%)] Loss: 0.082287 Train Epoch: 123 [18/20 (90%)] Loss: 0.086324 Test set: Avg. loss: 2.0232, Accuracy: 6360413/6599200 (96%)
Train Epoch: 124 [0/20 (0%)] Loss: 0.082141 Train Epoch: 124 [2/20 (10%)] Loss: 0.065022 Train Epoch: 124 [4/20 (20%)] Loss: 0.072926 Train Epoch: 124 [6/20 (30%)] Loss: 0.091893 Train Epoch: 124 [8/20 (40%)] Loss: 0.068101 Train Epoch: 124 [10/20 (50%)] Loss: 0.076435 Train Epoch: 124 [12/20 (60%)] Loss: 0.067058 Train Epoch: 124 [14/20 (70%)] Loss: 0.074494 Train Epoch: 124 [16/20 (80%)] Loss: 0.076324 Train Epoch: 124 [18/20 (90%)] Loss: 0.070061 Test set: Avg. loss: 2.0215, Accuracy: 6357588/6599200 (96%)
Train Epoch: 125 [0/20 (0%)] Loss: 0.067405 Train Epoch: 125 [2/20 (10%)] Loss: 0.069588 Train Epoch: 125 [4/20 (20%)] Loss: 0.060869 Train Epoch: 125 [6/20 (30%)] Loss: 0.077191 Train Epoch: 125 [8/20 (40%)] Loss: 0.061101 Train Epoch: 125 [10/20 (50%)] Loss: 0.103874 Train Epoch: 125 [12/20 (60%)] Loss: 0.062125 Train Epoch: 125 [14/20 (70%)] Loss: 0.065861 Train Epoch: 125 [16/20 (80%)] Loss: 0.076456 Train Epoch: 125 [18/20 (90%)] Loss: 0.111384 Test set: Avg. loss: 2.0269, Accuracy: 6358720/6599200 (96%)
Train Epoch: 126 [0/20 (0%)] Loss: 0.077660 Train Epoch: 126 [2/20 (10%)] Loss: 0.087964 Train Epoch: 126 [4/20 (20%)] Loss: 0.073029 Train Epoch: 126 [6/20 (30%)] Loss: 0.068447 Train Epoch: 126 [8/20 (40%)] Loss: 0.077593 Train Epoch: 126 [10/20 (50%)] Loss: 0.078313 Train Epoch: 126 [12/20 (60%)] Loss: 0.080314 Train Epoch: 126 [14/20 (70%)] Loss: 0.082276 Train Epoch: 126 [16/20 (80%)] Loss: 0.075382 Train Epoch: 126 [18/20 (90%)] Loss: 0.076842 Test set: Avg. loss: 2.0833, Accuracy: 6361426/6599200 (96%)
Train Epoch: 127 [0/20 (0%)] Loss: 0.078300 Train Epoch: 127 [2/20 (10%)] Loss: 0.072282 Train Epoch: 127 [4/20 (20%)] Loss: 0.088207 Train Epoch: 127 [6/20 (30%)] Loss: 0.084842 Train Epoch: 127 [8/20 (40%)] Loss: 0.085416 Train Epoch: 127 [10/20 (50%)] Loss: 0.077538 Train Epoch: 127 [12/20 (60%)] Loss: 0.098930 Train Epoch: 127 [14/20 (70%)] Loss: 0.056853 Train Epoch: 127 [16/20 (80%)] Loss: 0.060550 Train Epoch: 127 [18/20 (90%)] Loss: 0.126910 Test set: Avg. loss: 2.0992, Accuracy: 6358674/6599200 (96%)
Train Epoch: 128 [0/20 (0%)] Loss: 0.070952 Train Epoch: 128 [2/20 (10%)] Loss: 0.067823 Train Epoch: 128 [4/20 (20%)] Loss: 0.051658 Train Epoch: 128 [6/20 (30%)] Loss: 0.097680 Train Epoch: 128 [8/20 (40%)] Loss: 0.082746 Train Epoch: 128 [10/20 (50%)] Loss: 0.066513 Train Epoch: 128 [12/20 (60%)] Loss: 0.082688 Train Epoch: 128 [14/20 (70%)] Loss: 0.075280 Train Epoch: 128 [16/20 (80%)] Loss: 0.089693 Train Epoch: 128 [18/20 (90%)] Loss: 0.070161 Test set: Avg. loss: 2.0919, Accuracy: 6359711/6599200 (96%)
Train Epoch: 129 [0/20 (0%)] Loss: 0.079309 Train Epoch: 129 [2/20 (10%)] Loss: 0.065239 Train Epoch: 129 [4/20 (20%)] Loss: 0.076810 Train Epoch: 129 [6/20 (30%)] Loss: 0.055919 Train Epoch: 129 [8/20 (40%)] Loss: 0.072735 Train Epoch: 129 [10/20 (50%)] Loss: 0.070071 Train Epoch: 129 [12/20 (60%)] Loss: 0.072614 Train Epoch: 129 [14/20 (70%)] Loss: 0.087477 Train Epoch: 129 [16/20 (80%)] Loss: 0.059475 Train Epoch: 129 [18/20 (90%)] Loss: 0.072411 Test set: Avg. loss: 2.0568, Accuracy: 6356405/6599200 (96%)
Train Epoch: 130 [0/20 (0%)] Loss: 0.064642 Train Epoch: 130 [2/20 (10%)] Loss: 0.066408 Train Epoch: 130 [4/20 (20%)] Loss: 0.092804 Train Epoch: 130 [6/20 (30%)] Loss: 0.071496 Train Epoch: 130 [8/20 (40%)] Loss: 0.108724 Train Epoch: 130 [10/20 (50%)] Loss: 0.065672 Train Epoch: 130 [12/20 (60%)] Loss: 0.059262 Train Epoch: 130 [14/20 (70%)] Loss: 0.077336 Train Epoch: 130 [16/20 (80%)] Loss: 0.067516 Train Epoch: 130 [18/20 (90%)] Loss: 0.078497 Test set: Avg. loss: 2.1625, Accuracy: 6359829/6599200 (96%)
Train Epoch: 131 [0/20 (0%)] Loss: 0.067591 Train Epoch: 131 [2/20 (10%)] Loss: 0.067055 Train Epoch: 131 [4/20 (20%)] Loss: 0.067069 Train Epoch: 131 [6/20 (30%)] Loss: 0.072245 Train Epoch: 131 [8/20 (40%)] Loss: 0.075205 Train Epoch: 131 [10/20 (50%)] Loss: 0.056299 Train Epoch: 131 [12/20 (60%)] Loss: 0.057993 Train Epoch: 131 [14/20 (70%)] Loss: 0.071826 Train Epoch: 131 [16/20 (80%)] Loss: 0.056939 Train Epoch: 131 [18/20 (90%)] Loss: 0.080743 Test set: Avg. loss: 2.2175, Accuracy: 6361436/6599200 (96%)
Train Epoch: 132 [0/20 (0%)] Loss: 0.070024 Train Epoch: 132 [2/20 (10%)] Loss: 0.047789 Train Epoch: 132 [4/20 (20%)] Loss: 0.053560 Train Epoch: 132 [6/20 (30%)] Loss: 0.064002 Train Epoch: 132 [8/20 (40%)] Loss: 0.072863 Train Epoch: 132 [10/20 (50%)] Loss: 0.055466 Train Epoch: 132 [12/20 (60%)] Loss: 0.069337 Train Epoch: 132 [14/20 (70%)] Loss: 0.052692 Train Epoch: 132 [16/20 (80%)] Loss: 0.058998 Train Epoch: 132 [18/20 (90%)] Loss: 0.070125 Test set: Avg. loss: 2.1374, Accuracy: 6356921/6599200 (96%)
Train Epoch: 133 [0/20 (0%)] Loss: 0.062532 Train Epoch: 133 [2/20 (10%)] Loss: 0.052190 Train Epoch: 133 [4/20 (20%)] Loss: 0.074082 Train Epoch: 133 [6/20 (30%)] Loss: 0.055836 Train Epoch: 133 [8/20 (40%)] Loss: 0.063846 Train Epoch: 133 [10/20 (50%)] Loss: 0.076321 Train Epoch: 133 [12/20 (60%)] Loss: 0.082091 Train Epoch: 133 [14/20 (70%)] Loss: 0.077024 Train Epoch: 133 [16/20 (80%)] Loss: 0.047891 Train Epoch: 133 [18/20 (90%)] Loss: 0.057317 Test set: Avg. loss: 2.2158, Accuracy: 6356892/6599200 (96%)
Train Epoch: 134 [0/20 (0%)] Loss: 0.061615 Train Epoch: 134 [2/20 (10%)] Loss: 0.064712 Train Epoch: 134 [4/20 (20%)] Loss: 0.068349 Train Epoch: 134 [6/20 (30%)] Loss: 0.046329 Train Epoch: 134 [8/20 (40%)] Loss: 0.056383 Train Epoch: 134 [10/20 (50%)] Loss: 0.045070 Train Epoch: 134 [12/20 (60%)] Loss: 0.062432 Train Epoch: 134 [14/20 (70%)] Loss: 0.065614 Train Epoch: 134 [16/20 (80%)] Loss: 0.043516 Train Epoch: 134 [18/20 (90%)] Loss: 0.056645 Test set: Avg. loss: 2.1862, Accuracy: 6358757/6599200 (96%)
Train Epoch: 135 [0/20 (0%)] Loss: 0.049880 Train Epoch: 135 [2/20 (10%)] Loss: 0.047789 Train Epoch: 135 [4/20 (20%)] Loss: 0.047707 Train Epoch: 135 [6/20 (30%)] Loss: 0.054163 Train Epoch: 135 [8/20 (40%)] Loss: 0.037860 Train Epoch: 135 [10/20 (50%)] Loss: 0.053377 Train Epoch: 135 [12/20 (60%)] Loss: 0.038884 Train Epoch: 135 [14/20 (70%)] Loss: 0.052675 Train Epoch: 135 [16/20 (80%)] Loss: 0.056431 Train Epoch: 135 [18/20 (90%)] Loss: 0.053888 Test set: Avg. loss: 2.3290, Accuracy: 6360496/6599200 (96%)
Train Epoch: 136 [0/20 (0%)] Loss: 0.048306 Train Epoch: 136 [2/20 (10%)] Loss: 0.056736 Train Epoch: 136 [4/20 (20%)] Loss: 0.051285 Train Epoch: 136 [6/20 (30%)] Loss: 0.042218 Train Epoch: 136 [8/20 (40%)] Loss: 0.043307 Train Epoch: 136 [10/20 (50%)] Loss: 0.052174 Train Epoch: 136 [12/20 (60%)] Loss: 0.037518 Train Epoch: 136 [14/20 (70%)] Loss: 0.049847 Train Epoch: 136 [16/20 (80%)] Loss: 0.057029 Train Epoch: 136 [18/20 (90%)] Loss: 0.039228 Test set: Avg. loss: 2.3333, Accuracy: 6364106/6599200 (96%)
Train Epoch: 137 [0/20 (0%)] Loss: 0.045125 Train Epoch: 137 [2/20 (10%)] Loss: 0.039962 Train Epoch: 137 [4/20 (20%)] Loss: 0.046447 Train Epoch: 137 [6/20 (30%)] Loss: 0.048286 Train Epoch: 137 [8/20 (40%)] Loss: 0.051102 Train Epoch: 137 [10/20 (50%)] Loss: 0.044583 Train Epoch: 137 [12/20 (60%)] Loss: 0.049601 Train Epoch: 137 [14/20 (70%)] Loss: 0.069753 Train Epoch: 137 [16/20 (80%)] Loss: 0.041619 Train Epoch: 137 [18/20 (90%)] Loss: 0.051531 Test set: Avg. loss: 2.4028, Accuracy: 6360373/6599200 (96%)
Train Epoch: 138 [0/20 (0%)] Loss: 0.044408 Train Epoch: 138 [2/20 (10%)] Loss: 0.030238 Train Epoch: 138 [4/20 (20%)] Loss: 0.050891 Train Epoch: 138 [6/20 (30%)] Loss: 0.051938 Train Epoch: 138 [8/20 (40%)] Loss: 0.040395 Train Epoch: 138 [10/20 (50%)] Loss: 0.067628 Train Epoch: 138 [12/20 (60%)] Loss: 0.065993 Train Epoch: 138 [14/20 (70%)] Loss: 0.053658 Train Epoch: 138 [16/20 (80%)] Loss: 0.047352 Train Epoch: 138 [18/20 (90%)] Loss: 0.056188 Test set: Avg. loss: 2.3377, Accuracy: 6351834/6599200 (96%)
Train Epoch: 139 [0/20 (0%)] Loss: 0.054073 Train Epoch: 139 [2/20 (10%)] Loss: 0.037829 Train Epoch: 139 [4/20 (20%)] Loss: 0.061106 Train Epoch: 139 [6/20 (30%)] Loss: 0.045048 Train Epoch: 139 [8/20 (40%)] Loss: 0.049522 Train Epoch: 139 [10/20 (50%)] Loss: 0.045238 Train Epoch: 139 [12/20 (60%)] Loss: 0.046187 Train Epoch: 139 [14/20 (70%)] Loss: 0.057609 Train Epoch: 139 [16/20 (80%)] Loss: 0.061250 Train Epoch: 139 [18/20 (90%)] Loss: 0.046761 Test set: Avg. loss: 2.3881, Accuracy: 6357385/6599200 (96%)
Train Epoch: 140 [0/20 (0%)] Loss: 0.034573 Train Epoch: 140 [2/20 (10%)] Loss: 0.058391 Train Epoch: 140 [4/20 (20%)] Loss: 0.035924 Train Epoch: 140 [6/20 (30%)] Loss: 0.054874 Train Epoch: 140 [8/20 (40%)] Loss: 0.060791 Train Epoch: 140 [10/20 (50%)] Loss: 0.043008 Train Epoch: 140 [12/20 (60%)] Loss: 0.037790 Train Epoch: 140 [14/20 (70%)] Loss: 0.050782 Train Epoch: 140 [16/20 (80%)] Loss: 0.070952 Train Epoch: 140 [18/20 (90%)] Loss: 0.046277 Test set: Avg. loss: 2.4844, Accuracy: 6357142/6599200 (96%)
Train Epoch: 141 [0/20 (0%)] Loss: 0.038955 Train Epoch: 141 [2/20 (10%)] Loss: 0.065525 Train Epoch: 141 [4/20 (20%)] Loss: 0.051372 Train Epoch: 141 [6/20 (30%)] Loss: 0.043367 Train Epoch: 141 [8/20 (40%)] Loss: 0.045322 Train Epoch: 141 [10/20 (50%)] Loss: 0.055131 Train Epoch: 141 [12/20 (60%)] Loss: 0.054873 Train Epoch: 141 [14/20 (70%)] Loss: 0.047546 Train Epoch: 141 [16/20 (80%)] Loss: 0.048516 Train Epoch: 141 [18/20 (90%)] Loss: 0.042630 Test set: Avg. loss: 2.3840, Accuracy: 6362022/6599200 (96%)
Train Epoch: 142 [0/20 (0%)] Loss: 0.034853 Train Epoch: 142 [2/20 (10%)] Loss: 0.045175 Train Epoch: 142 [4/20 (20%)] Loss: 0.054012 Train Epoch: 142 [6/20 (30%)] Loss: 0.056285 Train Epoch: 142 [8/20 (40%)] Loss: 0.044885 Train Epoch: 142 [10/20 (50%)] Loss: 0.042529 Train Epoch: 142 [12/20 (60%)] Loss: 0.040000 Train Epoch: 142 [14/20 (70%)] Loss: 0.064590 Train Epoch: 142 [16/20 (80%)] Loss: 0.068431 Train Epoch: 142 [18/20 (90%)] Loss: 0.054453 Test set: Avg. loss: 2.4036, Accuracy: 6354836/6599200 (96%)
Train Epoch: 143 [0/20 (0%)] Loss: 0.058600 Train Epoch: 143 [2/20 (10%)] Loss: 0.083571 Train Epoch: 143 [4/20 (20%)] Loss: 0.068550 Train Epoch: 143 [6/20 (30%)] Loss: 0.050670 Train Epoch: 143 [8/20 (40%)] Loss: 0.160820 Train Epoch: 143 [10/20 (50%)] Loss: 0.058037 Train Epoch: 143 [12/20 (60%)] Loss: 0.045498 Train Epoch: 143 [14/20 (70%)] Loss: 0.094034 Train Epoch: 143 [16/20 (80%)] Loss: 0.068264 Train Epoch: 143 [18/20 (90%)] Loss: 0.059805 Test set: Avg. loss: 2.3292, Accuracy: 6355494/6599200 (96%)
Train Epoch: 144 [0/20 (0%)] Loss: 0.066386 Train Epoch: 144 [2/20 (10%)] Loss: 0.047336 Train Epoch: 144 [4/20 (20%)] Loss: 0.078884 Train Epoch: 144 [6/20 (30%)] Loss: 0.066797 Train Epoch: 144 [8/20 (40%)] Loss: 0.051565 Train Epoch: 144 [10/20 (50%)] Loss: 0.065232 Train Epoch: 144 [12/20 (60%)] Loss: 0.052769 Train Epoch: 144 [14/20 (70%)] Loss: 0.062003 Train Epoch: 144 [16/20 (80%)] Loss: 0.068887 Train Epoch: 144 [18/20 (90%)] Loss: 0.071155 Test set: Avg. loss: 2.3875, Accuracy: 6355269/6599200 (96%)
Train Epoch: 145 [0/20 (0%)] Loss: 0.057512 Train Epoch: 145 [2/20 (10%)] Loss: 0.035423 Train Epoch: 145 [4/20 (20%)] Loss: 0.065111 Train Epoch: 145 [6/20 (30%)] Loss: 0.067585 Train Epoch: 145 [8/20 (40%)] Loss: 0.043380 Train Epoch: 145 [10/20 (50%)] Loss: 0.051980 Train Epoch: 145 [12/20 (60%)] Loss: 0.042761 Train Epoch: 145 [14/20 (70%)] Loss: 0.041367 Train Epoch: 145 [16/20 (80%)] Loss: 0.048044 Train Epoch: 145 [18/20 (90%)] Loss: 0.051693 Test set: Avg. loss: 2.3792, Accuracy: 6361755/6599200 (96%)
Train Epoch: 146 [0/20 (0%)] Loss: 0.038373 Train Epoch: 146 [2/20 (10%)] Loss: 0.036963 Train Epoch: 146 [4/20 (20%)] Loss: 0.033163 Train Epoch: 146 [6/20 (30%)] Loss: 0.051134 Train Epoch: 146 [8/20 (40%)] Loss: 0.051273 Train Epoch: 146 [10/20 (50%)] Loss: 0.053202 Train Epoch: 146 [12/20 (60%)] Loss: 0.034431 Train Epoch: 146 [14/20 (70%)] Loss: 0.038894 Train Epoch: 146 [16/20 (80%)] Loss: 0.066008 Train Epoch: 146 [18/20 (90%)] Loss: 0.041706 Test set: Avg. loss: 2.3508, Accuracy: 6358105/6599200 (96%)
Train Epoch: 147 [0/20 (0%)] Loss: 0.050543 Train Epoch: 147 [2/20 (10%)] Loss: 0.038543 Train Epoch: 147 [4/20 (20%)] Loss: 0.037457 Train Epoch: 147 [6/20 (30%)] Loss: 0.047622 Train Epoch: 147 [8/20 (40%)] Loss: 0.037130 Train Epoch: 147 [10/20 (50%)] Loss: 0.035148 Train Epoch: 147 [12/20 (60%)] Loss: 0.055842 Train Epoch: 147 [14/20 (70%)] Loss: 0.036373 Train Epoch: 147 [16/20 (80%)] Loss: 0.043867 Train Epoch: 147 [18/20 (90%)] Loss: 0.044463 Test set: Avg. loss: 2.4135, Accuracy: 6360040/6599200 (96%)
Train Epoch: 148 [0/20 (0%)] Loss: 0.040937 Train Epoch: 148 [2/20 (10%)] Loss: 0.037625 Train Epoch: 148 [4/20 (20%)] Loss: 0.038821 Train Epoch: 148 [6/20 (30%)] Loss: 0.034218 Train Epoch: 148 [8/20 (40%)] Loss: 0.028354 Train Epoch: 148 [10/20 (50%)] Loss: 0.043532 Train Epoch: 148 [12/20 (60%)] Loss: 0.039851 Train Epoch: 148 [14/20 (70%)] Loss: 0.050641 Train Epoch: 148 [16/20 (80%)] Loss: 0.040226 Train Epoch: 148 [18/20 (90%)] Loss: 0.038224 Test set: Avg. loss: 2.4882, Accuracy: 6360136/6599200 (96%)
Train Epoch: 149 [0/20 (0%)] Loss: 0.043618 Train Epoch: 149 [2/20 (10%)] Loss: 0.033267 Train Epoch: 149 [4/20 (20%)] Loss: 0.043825 Train Epoch: 149 [6/20 (30%)] Loss: 0.030358 Train Epoch: 149 [8/20 (40%)] Loss: 0.035303 Train Epoch: 149 [10/20 (50%)] Loss: 0.054482 Train Epoch: 149 [12/20 (60%)] Loss: 0.036867 Train Epoch: 149 [14/20 (70%)] Loss: 0.033827 Train Epoch: 149 [16/20 (80%)] Loss: 0.037307 Train Epoch: 149 [18/20 (90%)] Loss: 0.027273 Test set: Avg. loss: 2.5489, Accuracy: 6362832/6599200 (96%)
Train Epoch: 150 [0/20 (0%)] Loss: 0.034501 Train Epoch: 150 [2/20 (10%)] Loss: 0.024792 Train Epoch: 150 [4/20 (20%)] Loss: 0.033409 Train Epoch: 150 [6/20 (30%)] Loss: 0.037115 Train Epoch: 150 [8/20 (40%)] Loss: 0.028760 Train Epoch: 150 [10/20 (50%)] Loss: 0.038453 Train Epoch: 150 [12/20 (60%)] Loss: 0.028453 Train Epoch: 150 [14/20 (70%)] Loss: 0.040448 Train Epoch: 150 [16/20 (80%)] Loss: 0.042665 Train Epoch: 150 [18/20 (90%)] Loss: 0.032891 Test set: Avg. loss: 2.5401, Accuracy: 6357816/6599200 (96%)
fig = plt.figure()
(train_counter, train_losses, test_losses) = torch.load( training_data_file)
test_counter = [i*len(train_loader.dataset) for i in range(len(test_losses))]
for i in range(len(train_losses)):
train_losses[i] = train_losses[i].cpu().detach()
for i in range(len(test_losses)):
test_losses[i] = test_losses[i].cpu().detach()
plt.plot(train_counter, train_losses, color='blue', label='Treinamento')
plt.plot(test_counter, test_losses, color='red', label='Teste')
plt.legend(['Train Loss', 'Test Loss'], loc='upper right')
plt.xlabel('number of training examples seen')
plt.ylabel('cross-entropy loss')
plt.legend()
plt.title("Rede convolucional")
print(f"Best: {min(test_losses)}")
Best: 0.6731995940208435
network.load_state_dict(torch.load(parameters_file))
fig = plt.figure()
length=5
begin=0
visualization_range = np.random.choice(len(test_data), length)
example_data = torch.stack([test_data[i][0] for i in visualization_range])
example_targets = torch.stack([test_data[i][1] for i in visualization_range])
network.eval()
plt.figure(figsize=(3*5, length*5))
for i in range(length):
print(example_data[i].shape)
data = example_data[i].movedim(0, 2)
print(data.shape)
plt.subplot(length,3,3*i+1)
plt.tight_layout()
plt.imshow(data)
plt.xticks([])
plt.yticks([])
target = example_targets[i]
print (target.shape)
plt.subplot(length,3,3*i+2)
plt.tight_layout()
plt.imshow(target, cmap='gray')
plt.xticks([])
plt.yticks([])
print(torch.unique(target))
with torch.no_grad():
output = network(example_data[i].to(device).unsqueeze(0)).squeeze(0)
print (output.shape)
plt.subplot(length,3,3*i+3)
plt.tight_layout()
plt.imshow(output.cpu().argmax(-3) , cmap='gray')
plt.xticks([])
plt.yticks([])
torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565]) torch.Size([3, 584, 565]) torch.Size([584, 565, 3]) torch.Size([584, 565]) tensor([0, 1]) torch.Size([2, 584, 565])
<Figure size 640x480 with 0 Axes>